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w1ndy/extract_vis_keywords.py

Created November 5, 2018 02:19
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Extract keywords from VIS paper titles and abstracts
Raw
extract_vis_keywords.py
import json
import sys
import string
from fuzzywuzzy import process
from nltk import word_tokenize, pos_tag
from nltk.corpus import stopwords
from collections import Counter
TITLES_JSON = 'vast2017titles.json'
ARTICLE_JSON = 'ieeetvcg201801.json'
IS_PREPRINT = False
CONFIDENCE_THRESHOLD = 90
KEYWORD_THRESHOLD = 5
VIS_STOP_WORDS = [
'analysis', 'data', 'model', 'visualization', 'models', 'analytics', 'users', 'experts', 'approach', 'exploration', 'process', 'domain', 'tool', 'framework', 'studies', 'techniques', 'paper', 'system', 'information', 'case', 'results', 'analysts', 'effectiveness', 'tools', 'applications', 'use', 'visualizations', 'systems', 'tasks', 'study', 'algorithms', 'va', 'methods', 'method', 'instances', 'evolution', 'algorithm', 'application', 'exchanges', 'interpretation', 'work', 'observations', 'behavior', 'knowledge', 'user', 'insights', 'features', 'ves', 'development', 'datasets', 'understanding', 'prediction', 'representation', 'context', 'feedback', 'number', 'interviews', 'structure', 'approaches', 'problem', 'solution', 'technique', 'changes', 'performance', 'views', 'result', 'scenarios', 'evaluation', 'comparison', 'state', 'workflow', 'design', 'set', 'domains', 'challenge', 'cases', 'expertise', 'nature', 'variety', 'researchers', 'structures', 'view', 'research', 'practitioners', 'relationships', 'participants', 'task', 'usability', 'scientists', 'dataset', 'practice', 'addition', 'overview', 'visual', 'however', 'demonstrate', 'two', 'novel', 'also', 'based', 'using', 'present', 'many', 'understand', 'large', 'developed', 'different', '\'s', 'explore', 'support', 'propose', 'multiple', 'new', 'often', 'three', 'provide', 'via', 'visualizing', 'current', 'various', 'thus', 'identify', 'existing', 'conducted', 'well', 'compare', 'help', 'within', 'need', 'one', 'important', 'efficient', 'expert', 'introduce', 'challenging', 'future', 'potential', 'designed', 'may', 'exploring', 'complex', 'individual', 'iterative', 'several', 'recent', 'exploratory', 'interactive', 'common', 'analytical', 'previous', 'feature', 'major', 'human', 'experience', 'due', 'ability', 'manipulation', 'interface', 'groups', 'hypotheses', 'interest', 'interactions', 'strategies', 'social', 'difficult', 'meaningful', 'analytic', 'usage', 'e.g.', 'interaction', 'particular', 'clusters', 'analyst', 'effective', 'similar', 'types', 'discovery', 'complexity', 'analysts', 'quality', 'significant', 'order', '“', '”'
]
REPLACE_WORD = {
'networks': 'network',
'clustering': 'cluster',
'clusters': 'cluster'
}
def main():
with open(TITLES_JSON) as f:
titles = json.loads(f.read())
with open(ARTICLE_JSON) as f:
articles = json.loads(f.read())['data']['preprints' if IS_PREPRINT else 'articles']
print('loaded %d paper titles and %d articles.' % (len(titles), len(articles)))
stop = stopwords.words('english') + list(string.punctuation) + VIS_STOP_WORDS
abstracts = {}
corpus = []
for pp in articles:
abstracts[pp['title']] = pp['abstract']
for t in titles:
choices = process.extract(t, abstracts.keys(), limit=5)
if not choices:
print('cannot find a match for paper "%s"' % t)
text = t.lower()
elif choices[0][0] != t and (choices[0][1] < CONFIDENCE_THRESHOLD):
print('cannot find a match for\npaper "%s".\nmaybe "%s"? (confidence = %d%%)\n' % (t, choices[0][0], choices[0][1]))
text = t.lower()
else:
text = (choices[0][0] + '\n' + abstracts[choices[0][0]]).lower()
tagged = pos_tag(word_tokenize(text))
keywords = set(map(lambda p: REPLACE_WORD[p[0]] if p[0] in REPLACE_WORD else p[0], filter(lambda p: (p[1].startswith('NN') or p[1].startswith('JJ')) and p[0] not in stop, tagged)))
corpus += keywords
freq = Counter()
for word in corpus:
freq.update([word])
keywords = list(filter(lambda p: p[1] >= KEYWORD_THRESHOLD, freq.most_common()))
print(keywords)
wordle = '\n'
for w, c in keywords:
w = w.replace('-', '')
for k in range(c):
wordle += w + ' '
print(wordle)
if __name__ == '__main__':
main()
Raw
ieeetvcg201801.json
{
"data": {
"issue": {
"id": "12OmNCaLEju",
"idPrefix": "tg",
"isPreviewOnly": false,
"issueNum": "01",
"pubType": "journal",
"downloadables": {
"hasCover": false,
"hasAzw3": false,
"hasEpub": true,
"hasMathEpub": false,
"hasMobi": true,
"hasPdf": true,
"hasZip": true,
"__typename": "PeriodicalIssueDownloadablesType"
},
"title": "Jan.",
"label": "Jan.",
"volume": "24",
"year": "2018",
"__typename": "PeriodicalIssue"
},
"articles": [{
"id": "13rRUwwJWFV",
"abstract": "Provides a listing of current staff, committee members and society officers.",
"authors": [],
"fno": "08167068",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "IEEE Transactions on Visualization and Computer Graphics",
"year": "2018",
"pages": "i-Bii",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyeTVi8",
"abstract": null,
"authors": [],
"fno": "08165931",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Contents",
"year": "2018",
"pages": "iii-ix",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxly8T3",
"abstract": null,
"authors": [{
"fullName": "Leila De Floriani",
"givenName": "Leila",
"surname": "De Floriani",
"__typename": "ArticleAuthorType"
}],
"fno": "08165928",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Message from the Editor-in-Chief",
"year": "2018",
"pages": "x-x",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwgQpqO",
"abstract": null,
"authors": [{
"fullName": "Tim Dwyer",
"givenName": "Tim",
"surname": "Dwyer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Niklas Elmqvist",
"givenName": "Niklas",
"surname": "Elmqvist",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Brian Fisher",
"givenName": "Brian",
"surname": "Fisher",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Steve Franconeri",
"givenName": "Steve",
"surname": "Franconeri",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ingrid Hotz",
"givenName": "Ingrid",
"surname": "Hotz",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Robert M. Mike Kirby",
"givenName": "Robert M. Mike",
"surname": "Kirby",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Shixia Liu",
"givenName": "Shixia",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tobias Schreck",
"givenName": "Tobias",
"surname": "Schreck",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xiaoru Yuan",
"givenName": "Xiaoru",
"surname": "Yuan",
"__typename": "ArticleAuthorType"
}],
"fno": "08167064",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Preface",
"year": "2018",
"pages": "xi-xv",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwcS1D2",
"abstract": null,
"authors": [{
"fullName": "Claudio T. Silva",
"givenName": "Claudio T.",
"surname": "Silva",
"__typename": "ArticleAuthorType"
}],
"fno": "08165937",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "IEEE Visualization and Graphics Technical Committee (VGTC)",
"year": "2018",
"pages": "xvi-xvi",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxjQybW",
"abstract": null,
"authors": [],
"fno": "08165925",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "VIS Conference Committee",
"year": "2018",
"pages": "xvii-xvii",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyv53FA",
"abstract": null,
"authors": [],
"fno": "08165935",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "VAST International Program Committee",
"year": "2018",
"pages": "xviii-xix",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUzp02oz",
"abstract": null,
"authors": [],
"fno": "08165930",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "VAST Steering Committee",
"year": "2018",
"pages": "xx-xx",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxASuvl",
"abstract": null,
"authors": [],
"fno": "08165934",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "VAST Paper Reviewers",
"year": "2018",
"pages": "xxi-xxv",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxASu0Q",
"abstract": "The 2017 Visualization Career Award goes to Charles (Chuck) Hansen in recognition for his contributions to large scale data visualization, including advances in parallel and volume rendering, novel interaction techniques, and techniques for exploiting hardware; for his leadership in the community as an educator, program chair, and editor; and for providing vision for the development and support of the field. The IEEE Visualization & Graphics Technical Committee (VGTC) is pleased to award Charles Hansen the 2017 Visualization Career Award.",
"authors": [{
"fullName": "Charles Hansen",
"givenName": "Charles",
"surname": "Hansen",
"__typename": "ArticleAuthorType"
}],
"fno": "08165941",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "The 2017 Visualization Career Award",
"year": "2018",
"pages": "xxvi-xxvi",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxOve9P",
"abstract": "The 2017 Visualization Technical Achievement Award goes to Jeffrey Heer in recognition of his work on the design, development, dissemination, and popularization of languages for visualization. The IEEE Visualization & Graphics Technical Committee (VGTC) is pleased to award Jeffrey Heer the 2017 Visualization Technical Achievement Award.",
"authors": [{
"fullName": "Jeffrey Heer",
"givenName": "Jeffrey",
"surname": "Heer",
"__typename": "ArticleAuthorType"
}],
"fno": "08165929",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "The 2017 Visualization Technical Achievement Award",
"year": "2018",
"pages": "xxvii-xxviii",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwbs2gx",
"abstract": "We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.",
"authors": [{
"fullName": "Kanit Wongsuphasawat",
"givenName": "Kanit",
"surname": "Wongsuphasawat",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Smilkov",
"givenName": "Daniel",
"surname": "Smilkov",
"__typename": "ArticleAuthorType"
}, {
"fullName": "James Wexler",
"givenName": "James",
"surname": "Wexler",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jimbo Wilson",
"givenName": "Jimbo",
"surname": "Wilson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Dandelion Mane",
"givenName": "Dandelion",
"surname": "Mane",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Doug Fritz",
"givenName": "Doug",
"surname": "Fritz",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Dilip Krishnan",
"givenName": "Dilip",
"surname": "Krishnan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Fernanda B. Viegas",
"givenName": "Fernanda B.",
"surname": "Viegas",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Martin Wattenberg",
"givenName": "Martin",
"surname": "Wattenberg",
"__typename": "ArticleAuthorType"
}],
"fno": "08019861",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow",
"year": "2018",
"pages": "1-12",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUytF41G",
"abstract": "Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.",
"authors": [{
"fullName": "Manuel Stein",
"givenName": "Manuel",
"surname": "Stein",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Halldor Janetzko",
"givenName": "Halldor",
"surname": "Janetzko",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Andreas Lamprecht",
"givenName": "Andreas",
"surname": "Lamprecht",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Thorsten Breitkreutz",
"givenName": "Thorsten",
"surname": "Breitkreutz",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Philipp Zimmermann",
"givenName": "Philipp",
"surname": "Zimmermann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Bastian Goldlucke",
"givenName": "Bastian",
"surname": "Goldlucke",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tobias Schreck",
"givenName": "Tobias",
"surname": "Schreck",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Gennady Andrienko",
"givenName": "Gennady",
"surname": "Andrienko",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Grossniklaus",
"givenName": "Michael",
"surname": "Grossniklaus",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel A. Keim",
"givenName": "Daniel A.",
"surname": "Keim",
"__typename": "ArticleAuthorType"
}],
"fno": "08019849",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis",
"year": "2018",
"pages": "13-22",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyogGAi",
"abstract": "The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the “smart city” as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.",
"authors": [{
"fullName": "Nan Cao",
"givenName": "Nan",
"surname": "Cao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Chaoguang Lin",
"givenName": "Chaoguang",
"surname": "Lin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Qiuhan Zhu",
"givenName": "Qiuhan",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yu-Ru Lin",
"givenName": "Yu-Ru",
"surname": "Lin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xian Teng",
"givenName": "Xian",
"surname": "Teng",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xidao Wen",
"givenName": "Xidao",
"surname": "Wen",
"__typename": "ArticleAuthorType"
}],
"fno": "08022952",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data",
"year": "2018",
"pages": "23-33",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyY294H",
"abstract": "Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.",
"authors": [{
"fullName": "Gennady Andrienko",
"givenName": "Gennady",
"surname": "Andrienko",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Natalia Andrienko",
"givenName": "Natalia",
"surname": "Andrienko",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Georg Fuchs",
"givenName": "Georg",
"surname": "Fuchs",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jose Manuel Cordero Garcia",
"givenName": "Jose Manuel Cordero",
"surname": "Garcia",
"__typename": "ArticleAuthorType"
}],
"fno": "08017616",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Clustering Trajectories by Relevant Parts for Air Traffic Analysis",
"year": "2018",
"pages": "34-44",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyft7D8",
"abstract": "Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.",
"authors": [{
"fullName": "Yuanzhe Chen",
"givenName": "Yuanzhe",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Panpan Xu",
"givenName": "Panpan",
"surname": "Xu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Liu Ren",
"givenName": "Liu",
"surname": "Ren",
"__typename": "ArticleAuthorType"
}],
"fno": "08025640",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Sequence Synopsis: Optimize Visual Summary of Temporal Event Data",
"year": "2018",
"pages": "45-55",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwkxc5r",
"abstract": "Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.",
"authors": [{
"fullName": "Shunan Guo",
"givenName": "Shunan",
"surname": "Guo",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ke Xu",
"givenName": "Ke",
"surname": "Xu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Rongwen Zhao",
"givenName": "Rongwen",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "David Gotz",
"givenName": "David",
"surname": "Gotz",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hongyuan Zha",
"givenName": "Hongyuan",
"surname": "Zha",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Nan Cao",
"givenName": "Nan",
"surname": "Cao",
"__typename": "ArticleAuthorType"
}],
"fno": "08017612",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "EventThread: Visual Summarization and Stage Analysis of Event Sequence Data",
"year": "2018",
"pages": "56-65",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUygBw7g",
"abstract": "This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain.",
"authors": [{
"fullName": "Andrea Unger",
"givenName": "Andrea",
"surname": "Unger",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Nadine Drager",
"givenName": "Nadine",
"surname": "Drager",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Mike Sips",
"givenName": "Mike",
"surname": "Sips",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Dirk J. Lehmann",
"givenName": "Dirk J.",
"surname": "Lehmann",
"__typename": "ArticleAuthorType"
}],
"fno": "08022969",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Understanding a Sequence of Sequences: Visual Exploration of Categorical States in Lake Sediment Cores",
"year": "2018",
"pages": "66-76",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxAATgA",
"abstract": "Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs.",
"authors": [{
"fullName": "Mengchen Liu",
"givenName": "Mengchen",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jiaxin Shi",
"givenName": "Jiaxin",
"surname": "Shi",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kelei Cao",
"givenName": "Kelei",
"surname": "Cao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jun Zhu",
"givenName": "Jun",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Shixia Liu",
"givenName": "Shixia",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}],
"fno": "08019879",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Analyzing the Training Processes of Deep Generative Models",
"year": "2018",
"pages": "77-87",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyogGAh",
"abstract": "While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models.",
"authors": [{
"fullName": "Minsuk Kahng",
"givenName": "Minsuk",
"surname": "Kahng",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Pierre Y. Andrews",
"givenName": "Pierre Y.",
"surname": "Andrews",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Aditya Kalro",
"givenName": "Aditya",
"surname": "Kalro",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Duen Horng Polo Chau",
"givenName": "Duen Horng Polo",
"surname": "Chau",
"__typename": "ArticleAuthorType"
}],
"fno": "08022871",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models",
"year": "2018",
"pages": "88-97",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxlgxTs",
"abstract": "Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.",
"authors": [{
"fullName": "Nicola Pezzotti",
"givenName": "Nicola",
"surname": "Pezzotti",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Thomas Hollt",
"givenName": "Thomas",
"surname": "Hollt",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jan Van Gemert",
"givenName": "Jan",
"surname": "Van Gemert",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Boudewijn P.F. Lelieveldt",
"givenName": "Boudewijn P.F.",
"surname": "Lelieveldt",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Elmar Eisemann",
"givenName": "Elmar",
"surname": "Eisemann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Anna Vilanova",
"givenName": "Anna",
"surname": "Vilanova",
"__typename": "ArticleAuthorType"
}],
"fno": "08019872",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks",
"year": "2018",
"pages": "98-108",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyv53Fz",
"abstract": "In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we — a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of “Tropical Cyclone Karl”, guiding the user towards the cluster robustness information required for subsequent ensemble analysis.",
"authors": [{
"fullName": "Alexander Kumpf",
"givenName": "Alexander",
"surname": "Kumpf",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Bianca Tost",
"givenName": "Bianca",
"surname": "Tost",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Marlene Baumgart",
"givenName": "Marlene",
"surname": "Baumgart",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Riemer",
"givenName": "Michael",
"surname": "Riemer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Rudiger Westermann",
"givenName": "Rudiger",
"surname": "Westermann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Marc Rautenhaus",
"givenName": "Marc",
"surname": "Rautenhaus",
"__typename": "ArticleAuthorType"
}],
"fno": "08019883",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses",
"year": "2018",
"pages": "109-119",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxD9gXO",
"abstract": "Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.",
"authors": [{
"fullName": "Dominik Sacha",
"givenName": "Dominik",
"surname": "Sacha",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Matthias Kraus",
"givenName": "Matthias",
"surname": "Kraus",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jurgen Bernard",
"givenName": "Jurgen",
"surname": "Bernard",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Behrisch",
"givenName": "Michael",
"surname": "Behrisch",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tobias Schreck",
"givenName": "Tobias",
"surname": "Schreck",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yuki Asano",
"givenName": "Yuki",
"surname": "Asano",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel A. Keim",
"givenName": "Daniel A.",
"surname": "Keim",
"__typename": "ArticleAuthorType"
}],
"fno": "08019867",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance",
"year": "2018",
"pages": "120-130",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUIJuxvq",
"abstract": "Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.",
"authors": [{
"fullName": "John Wenskovitch",
"givenName": "John",
"surname": "Wenskovitch",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ian Crandell",
"givenName": "Ian",
"surname": "Crandell",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Naren Ramakrishnan",
"givenName": "Naren",
"surname": "Ramakrishnan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Leanna House",
"givenName": "Leanna",
"surname": "House",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Scotland Leman",
"givenName": "Scotland",
"surname": "Leman",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Chris North",
"givenName": "Chris",
"surname": "North",
"__typename": "ArticleAuthorType"
}],
"fno": "08019882",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics",
"year": "2018",
"pages": "131-141",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwInv4t",
"abstract": "Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.",
"authors": [{
"fullName": "Bum Chul Kwon",
"givenName": "Bum Chul",
"surname": "Kwon",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ben Eysenbach",
"givenName": "Ben",
"surname": "Eysenbach",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Janu Verma",
"givenName": "Janu",
"surname": "Verma",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kenney Ng",
"givenName": "Kenney",
"surname": "Ng",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Christopher De Filippi",
"givenName": "Christopher",
"surname": "De Filippi",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Walter F. Stewart",
"givenName": "Walter F.",
"surname": "Stewart",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Adam Perer",
"givenName": "Adam",
"surname": "Perer",
"__typename": "ArticleAuthorType"
}],
"fno": "08019866",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Clustervision: Visual Supervision of Unsupervised Clustering",
"year": "2018",
"pages": "142-151",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwghd55",
"abstract": "Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.",
"authors": [{
"fullName": "Alsallakh Bilal",
"givenName": "Alsallakh",
"surname": "Bilal",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Amin Jourabloo",
"givenName": "Amin",
"surname": "Jourabloo",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Mao Ye",
"givenName": "Mao",
"surname": "Ye",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xiaoming Liu",
"givenName": "Xiaoming",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Liu Ren",
"givenName": "Liu",
"surname": "Ren",
"__typename": "ArticleAuthorType"
}],
"fno": "08017618",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Do Convolutional Neural Networks Learn Class Hierarchy?",
"year": "2018",
"pages": "152-162",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwj7cph",
"abstract": "Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms.",
"authors": [{
"fullName": "Shixia Liu",
"givenName": "Shixia",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jiannan Xiao",
"givenName": "Jiannan",
"surname": "Xiao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Junlin Liu",
"givenName": "Junlin",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xiting Wang",
"givenName": "Xiting",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jing Wu",
"givenName": "Jing",
"surname": "Wu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jun Zhu",
"givenName": "Jun",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
}],
"fno": "08017582",
"isOpenAccess": true,
"issueNum": "01",
"sectionTitle": null,
"title": "Visual Diagnosis of Tree Boosting Methods",
"year": "2018",
"pages": "163-173",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxYINfl",
"abstract": "Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.",
"authors": [{
"fullName": "Thomas Muhlbacher",
"givenName": "Thomas",
"surname": "Muhlbacher",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Lorenz Linhardt",
"givenName": "Lorenz",
"surname": "Linhardt",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Torsten Moller",
"givenName": "Torsten",
"surname": "Moller",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Harald Piringer",
"givenName": "Harald",
"surname": "Piringer",
"__typename": "ArticleAuthorType"
}],
"fno": "08019878",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees",
"year": "2018",
"pages": "174-183",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxBa56b",
"abstract": "We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.",
"authors": [{
"fullName": "Angus G. Forbes",
"givenName": "Angus G.",
"surname": "Forbes",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Andrew Burks",
"givenName": "Andrew",
"surname": "Burks",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kristine Lee",
"givenName": "Kristine",
"surname": "Lee",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xing Li",
"givenName": "Xing",
"surname": "Li",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Pierre Boutillier",
"givenName": "Pierre",
"surname": "Boutillier",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jean Krivine",
"givenName": "Jean",
"surname": "Krivine",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Walter Fontana",
"givenName": "Walter",
"surname": "Fontana",
"__typename": "ArticleAuthorType"
}],
"fno": "08017593",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Dynamic Influence Networks for Rule-Based Models",
"year": "2018",
"pages": "184-194",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxASuAA",
"abstract": "Discovering and analyzing biclusters, i.e., two sets of related entities with close relationships, is a critical task in many real-world applications, such as exploring entity co-occurrences in intelligence analysis, and studying gene expression in bio-informatics. While the output of biclustering techniques can offer some initial low-level insights, visual approaches are required on top of that due to the algorithmic output complexity. This paper proposes a visualization technique, called BiDots, that allows analysts to interactively explore biclusters over multiple domains. BiDots overcomes several limitations of existing bicluster visualizations by encoding biclusters in a more compact and cluster-driven manner. A set of handy interactions is incorporated to support flexible analysis of biclustering results. More importantly, BiDots addresses the cases of weighted biclusters, which has been underexploited in the literature. The design of BiDots is grounded by a set of analytical tasks derived from previous work. We demonstrate its usefulness and effectiveness for exploring computed biclusters with an investigative document analysis task, in which suspicious people and activities are identified from a text corpus.",
"authors": [{
"fullName": "Jian Zhao",
"givenName": "Jian",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Maoyuan Sun",
"givenName": "Maoyuan",
"surname": "Sun",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Francine Chen",
"givenName": "Francine",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Patrick Chiu",
"givenName": "Patrick",
"surname": "Chiu",
"__typename": "ArticleAuthorType"
}],
"fno": "08017581",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "BiDots: Visual Exploration of Weighted Biclusters",
"year": "2018",
"pages": "195-204",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwcAqql",
"abstract": "Whether and how does the structure of family trees differ by ancestral traits over generations? This is a fundamental question regarding the structural heterogeneity of family trees for the multi-generational transmission research. However, previous work mostly focuses on parent-child scenarios due to the lack of proper tools to handle the complexity of extending the research to multi-generational processes. Through an iterative design study with social scientists and historians, we develop TreeEvo that assists users to generate and test empirical hypotheses for multi-generational research. TreeEvo summarizes and organizes family trees by structural features in a dynamic manner based on a traditional Sankey diagram. A pixel-based technique is further proposed to compactly encode trees with complex structures in each Sankey Node. Detailed information of trees is accessible through a space-efficient visualization with semantic zooming. Moreover, TreeEvo embeds Multinomial Logit Model (MLM) to examine statistical associations between tree structure and ancestral traits. We demonstrate the effectiveness and usefulness of TreeEvo through an in-depth case-study with domain experts using a real-world dataset (containing 54,128 family trees of 126,196 individuals).",
"authors": [{
"fullName": "Siwei Fu",
"givenName": "Siwei",
"surname": "Fu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hao Dong",
"givenName": "Hao",
"surname": "Dong",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Weiwei Cui",
"givenName": "Weiwei",
"surname": "Cui",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jian Zhao",
"givenName": "Jian",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Huamin Qu",
"givenName": "Huamin",
"surname": "Qu",
"__typename": "ArticleAuthorType"
}],
"fno": "08017631",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "How Do Ancestral Traits Shape Family Trees Over Generations?",
"year": "2018",
"pages": "205-214",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUy0qnGp",
"abstract": "Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.",
"authors": [{
"fullName": "Robert Pienta",
"givenName": "Robert",
"surname": "Pienta",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Fred Hohman",
"givenName": "Fred",
"surname": "Hohman",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alex Endert",
"givenName": "Alex",
"surname": "Endert",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Acar Tamersoy",
"givenName": "Acar",
"surname": "Tamersoy",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kevin Roundy",
"givenName": "Kevin",
"surname": "Roundy",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Chris Gates",
"givenName": "Chris",
"surname": "Gates",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Shamkant Navathe",
"givenName": "Shamkant",
"surname": "Navathe",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Duen Horng Chau",
"givenName": "Duen Horng",
"surname": "Chau",
"__typename": "ArticleAuthorType"
}],
"fno": "08019832",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "VIGOR: Interactive Visual Exploration of Graph Query Results",
"year": "2018",
"pages": "215-225",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUIJuxpD",
"abstract": "Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.",
"authors": [{
"fullName": "Arjun Srinivasan",
"givenName": "Arjun",
"surname": "Srinivasan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hyunwoo Park",
"givenName": "Hyunwoo",
"surname": "Park",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alex Endert",
"givenName": "Alex",
"surname": "Endert",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Rahul C. Basole",
"givenName": "Rahul C.",
"surname": "Basole",
"__typename": "ArticleAuthorType"
}],
"fno": "08019835",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Graphiti: Interactive Specification of Attribute-Based Edges for Network Modeling and Visualization",
"year": "2018",
"pages": "226-235",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxBJhmV",
"abstract": "Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the $x$ axis and $y$ axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS ($x$ axis) and the variation of LTS in structures (the combination of $x$ axis and $y$ axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.",
"authors": [{
"fullName": "Jiazhi Xia",
"givenName": "Jiazhi",
"surname": "Xia",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Fenjin Ye",
"givenName": "Fenjin",
"surname": "Ye",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Wei Chen",
"givenName": "Wei",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yusi Wang",
"givenName": "Yusi",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Weifeng Chen",
"givenName": "Weifeng",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yuxin Ma",
"givenName": "Yuxin",
"surname": "Ma",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Anthony K.H. Tung",
"givenName": "Anthony K.H.",
"surname": "Tung",
"__typename": "ArticleAuthorType"
}],
"fno": "08017645",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets",
"year": "2018",
"pages": "236-245",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUygT7n3",
"abstract": "Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.",
"authors": [{
"fullName": "Xun Zhao",
"givenName": "Xun",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yanhong Wu",
"givenName": "Yanhong",
"surname": "Wu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Weiwei Cui",
"givenName": "Weiwei",
"surname": "Cui",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xinnan Du",
"givenName": "Xinnan",
"surname": "Du",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yuan Chen",
"givenName": "Yuan",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yong Wang",
"givenName": "Yong",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Dik Lun Lee",
"givenName": "Dik Lun",
"surname": "Lee",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Huamin Qu",
"givenName": "Huamin",
"surname": "Qu",
"__typename": "ArticleAuthorType"
}],
"fno": "08019873",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data",
"year": "2018",
"pages": "246-255",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxD9h5e",
"abstract": "Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-$n$ (many rows of data), d) dealing with outliers that mask other outliers, and e) dealing consistently with unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, hdoutliers is based on a distributional model that allows outliers to be tagged with a probability. This critical feature reduces the likelihood of false discoveries.",
"authors": [{
"fullName": "Leland Wilkinson",
"givenName": "Leland",
"surname": "Wilkinson",
"__typename": "ArticleAuthorType"
}],
"fno": "08019881",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Visualizing Big Data Outliers Through Distributed Aggregation",
"year": "2018",
"pages": "256-266",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwdrdSD",
"abstract": "As Visual Analytics (VA) research grows and diversifies to encompass new systems, techniques, and use contexts, gaining a holistic view of analytic practices is becoming ever more challenging. However, such a view is essential for researchers and practitioners seeking to develop systems for broad audiences that span multiple domains. In this paper, we interpret VA research through the lens of Activity Theory (AT)—a framework for modelling human activities that has been influential in the field of Human-Computer Interaction. We first provide an overview of Activity Theory, showing its potential for thinking beyond tasks, representations, and interactions to the broader systems of activity in which interactive tools are embedded and used. Next, we describe how Activity Theory can be used as an organizing framework in the construction of activity typologies, building and expanding upon the tradition of abstract task taxonomies in the field of Information Visualization. We then apply the resulting process to create an activity typology for Visual Analytics, synthesizing a wide range of systems and activity concepts from the literature. Finally, we use this typology as the foundation of an activity-centered design process, highlighting both tensions and opportunities in the design space of VA systems.",
"authors": [{
"fullName": "Darren Edge",
"givenName": "Darren",
"surname": "Edge",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Nathalie Henry Riche",
"givenName": "Nathalie Henry",
"surname": "Riche",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jonathan Larson",
"givenName": "Jonathan",
"surname": "Larson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Christopher White",
"givenName": "Christopher",
"surname": "White",
"__typename": "ArticleAuthorType"
}],
"fno": "08019880",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Beyond Tasks: An Activity Typology for Visual Analytics",
"year": "2018",
"pages": "267-277",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxC0SWe",
"abstract": "Data scientists and other analytic professionals often use interactive visualization in the dissemination phase at the end of a workflow during which findings are communicated to a wider audience. Visualization scientists, however, hold that interactive representation of data can also be used during exploratory analysis itself. Since the use of interactive visualization is optional rather than mandatory, this leaves a “visualization gap” during initial exploratory analysis that is the onus of visualization researchers to fill. In this paper, we explore areas where visualization would be beneficial in applied research by conducting a design study using a novel variation on contextual inquiry conducted with professional data analysts. Based on these interviews and experiments, we propose a set of interactive initial exploratory visualization guidelines which we believe will promote adoption by this type of user.",
"authors": [{
"fullName": "Andrea Batch",
"givenName": "Andrea",
"surname": "Batch",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Niklas Elmqvist",
"givenName": "Niklas",
"surname": "Elmqvist",
"__typename": "ArticleAuthorType"
}],
"fno": "08017577",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "The Interactive Visualization Gap in Initial Exploratory Data Analysis",
"year": "2018",
"pages": "278-287",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwwaKtd",
"abstract": "People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas.",
"authors": [{
"fullName": "Emily Wall",
"givenName": "Emily",
"surname": "Wall",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Subhajit Das",
"givenName": "Subhajit",
"surname": "Das",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ravish Chawla",
"givenName": "Ravish",
"surname": "Chawla",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Bharath Kalidindi",
"givenName": "Bharath",
"surname": "Kalidindi",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Eli T. Brown",
"givenName": "Eli T.",
"surname": "Brown",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alex Endert",
"givenName": "Alex",
"surname": "Endert",
"__typename": "ArticleAuthorType"
}],
"fno": "08019863",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Podium: Ranking Data Using Mixed-Initiative Visual Analytics",
"year": "2018",
"pages": "288-297",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxBrGh7",
"abstract": "Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.",
"authors": [{
"fullName": "Jurgen Bernard",
"givenName": "Jurgen",
"surname": "Bernard",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Marco Hutter",
"givenName": "Marco",
"surname": "Hutter",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Matthias Zeppelzauer",
"givenName": "Matthias",
"surname": "Zeppelzauer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Dieter Fellner",
"givenName": "Dieter",
"surname": "Fellner",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Sedlmair",
"givenName": "Michael",
"surname": "Sedlmair",
"__typename": "ArticleAuthorType"
}],
"fno": "08019851",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study",
"year": "2018",
"pages": "298-308",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUNvgz9X",
"abstract": "Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools.",
"authors": [{
"fullName": "Enamul Hoque",
"givenName": "Enamul",
"surname": "Hoque",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Vidya Setlur",
"givenName": "Vidya",
"surname": "Setlur",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Melanie Tory",
"givenName": "Melanie",
"surname": "Tory",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Isaac Dykeman",
"givenName": "Isaac",
"surname": "Dykeman",
"__typename": "ArticleAuthorType"
}],
"fno": "08019833",
"isOpenAccess": true,
"issueNum": "01",
"sectionTitle": null,
"title": "Applying Pragmatics Principles for Interaction with Visual Analytics",
"year": "2018",
"pages": "309-318",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwgQpDy",
"abstract": "The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose “black-box” solver. In practice, however, many problems cannot be solved completely automatically, but require a “human-in-the-loop” to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient's tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem.",
"authors": [{
"fullName": "Jie Liu",
"givenName": "Jie",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tim Dwyer",
"givenName": "Tim",
"surname": "Dwyer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kim Marriott",
"givenName": "Kim",
"surname": "Marriott",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jeremy Millar",
"givenName": "Jeremy",
"surname": "Millar",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Annette Haworth",
"givenName": "Annette",
"surname": "Haworth",
"__typename": "ArticleAuthorType"
}],
"fno": "08017652",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Understanding the Relationship Between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy",
"year": "2018",
"pages": "319-329",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxNEqQ0",
"abstract": "Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.",
"authors": [{
"fullName": "Roger A. Leite",
"givenName": "Roger A.",
"surname": "Leite",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Theresia Gschwandtner",
"givenName": "Theresia",
"surname": "Gschwandtner",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Silvia Miksch",
"givenName": "Silvia",
"surname": "Miksch",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Simone Kriglstein",
"givenName": "Simone",
"surname": "Kriglstein",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Margit Pohl",
"givenName": "Margit",
"surname": "Pohl",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Erich Gstrein",
"givenName": "Erich",
"surname": "Gstrein",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Johannes Kuntner",
"givenName": "Johannes",
"surname": "Kuntner",
"__typename": "ArticleAuthorType"
}],
"fno": "08023780",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "EVA: Visual Analytics to Identify Fraudulent Events",
"year": "2018",
"pages": "330-339",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUytWF9s",
"abstract": "During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst's interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.",
"authors": [{
"fullName": "Jian Zhao",
"givenName": "Jian",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Glueck",
"givenName": "Michael",
"surname": "Glueck",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Petra Isenberg",
"givenName": "Petra",
"surname": "Isenberg",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Fanny Chevalier",
"givenName": "Fanny",
"surname": "Chevalier",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Azam Khan",
"givenName": "Azam",
"surname": "Khan",
"__typename": "ArticleAuthorType"
}],
"fno": "08017596",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs",
"year": "2018",
"pages": "340-350",
"pubType": "trans",
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}, {
"id": "13rRUxE04tI",
"abstract": "Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.",
"authors": [{
"fullName": "Xumeng Wang",
"givenName": "Xumeng",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jia-Kai Chou",
"givenName": "Jia-Kai",
"surname": "Chou",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Wei Chen",
"givenName": "Wei",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Huihua Guan",
"givenName": "Huihua",
"surname": "Guan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Wenlong Chen",
"givenName": "Wenlong",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tianyi Lao",
"givenName": "Tianyi",
"surname": "Lao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kwan-Liu Ma",
"givenName": "Kwan-Liu",
"surname": "Ma",
"__typename": "ArticleAuthorType"
}],
"fno": "08019828",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data",
"year": "2018",
"pages": "351-360",
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}, {
"id": "13rRUwbaqUU",
"abstract": "Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.",
"authors": [{
"fullName": "Deokgun Park",
"givenName": "Deokgun",
"surname": "Park",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Seungyeon Kim",
"givenName": "Seungyeon",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jurim Lee",
"givenName": "Jurim",
"surname": "Lee",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jaegul Choo",
"givenName": "Jaegul",
"surname": "Choo",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Nicholas Diakopoulos",
"givenName": "Nicholas",
"surname": "Diakopoulos",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Niklas Elmqvist",
"givenName": "Niklas",
"surname": "Elmqvist",
"__typename": "ArticleAuthorType"
}],
"fno": "08023823",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding",
"year": "2018",
"pages": "361-370",
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}, {
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"abstract": "PhenoLines is a visual analysis tool for the interpretation of disease subtypes, derived from the application of topic models to clinical data. Topic models enable one to mine cross-sectional patient comorbidity data (e.g., electronic health records) and construct disease subtypes—each with its own temporally evolving prevalence and co-occurrence of phenotypes—without requiring aligned longitudinal phenotype data for all patients. However, the dimensionality of topic models makes interpretation challenging, and de facto analyses provide little intuition regarding phenotype relevance or phenotype interrelationships. PhenoLines enables one to compare phenotype prevalence within and across disease subtype topics, thus supporting subtype characterization, a task that involves identifying a proposed subtype's dominant phenotypes, ages of effect, and clinical validity. We contribute a data transformation workflow that employs the Human Phenotype Ontology to hierarchically organize phenotypes and aggregate the evolving probabilities produced by topic models. We introduce a novel measure of phenotype relevance that can be used to simplify the resulting topology. The design of PhenoLines was motivated by formative interviews with machine learning and clinical experts. We describe the collaborative design process, distill high-level tasks, and report on initial evaluations with machine learning experts and a medical domain expert. These results suggest that PhenoLines demonstrates promising approaches to support the characterization and optimization of topic models.",
"authors": [{
"fullName": "Michael Glueck",
"givenName": "Michael",
"surname": "Glueck",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Mahdi Pakdaman Naeini",
"givenName": "Mahdi Pakdaman",
"surname": "Naeini",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Finale Doshi-Velez",
"givenName": "Finale",
"surname": "Doshi-Velez",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Fanny Chevalier",
"givenName": "Fanny",
"surname": "Chevalier",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Azam Khan",
"givenName": "Azam",
"surname": "Khan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Wigdor",
"givenName": "Daniel",
"surname": "Wigdor",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Brudno",
"givenName": "Michael",
"surname": "Brudno",
"__typename": "ArticleAuthorType"
}],
"fno": "08019821",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models",
"year": "2018",
"pages": "371-381",
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}, {
"id": "13rRUwghd9c",
"abstract": "Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.",
"authors": [{
"fullName": "Mennatallah El-Assady",
"givenName": "Mennatallah",
"surname": "El-Assady",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Rita Sevastjanova",
"givenName": "Rita",
"surname": "Sevastjanova",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Fabian Sperrle",
"givenName": "Fabian",
"surname": "Sperrle",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Keim",
"givenName": "Daniel",
"surname": "Keim",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Christopher Collins",
"givenName": "Christopher",
"surname": "Collins",
"__typename": "ArticleAuthorType"
}],
"fno": "08019825",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework",
"year": "2018",
"pages": "382-391",
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}, {
"id": "13rRUB7a1fY",
"abstract": "Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions. These limitations may cause data misinterpretation in visualizations, which frequently use small, elongated marks. Our goal is to develop quantitative metrics to help people use color more effectively in visualizations. We present a series of crowdsourced studies measuring color difference perceptions for three common mark types: points, bars, and lines. Our results indicate that peoples' abilities to perceive color differences varies significantly across mark types. Probabilistic models constructed from the resulting data can provide objective guidance for designers, allowing them to anticipate viewer perceptions in order to inform effective encoding design.",
"authors": [{
"fullName": "Danielle Albers Szafir",
"givenName": "Danielle Albers",
"surname": "Szafir",
"__typename": "ArticleAuthorType"
}],
"fno": "08017604",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Modeling Color Difference for Visualization Design",
"year": "2018",
"pages": "392-401",
"pubType": "trans",
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}, {
"id": "13rRUy3gn7C",
"abstract": "Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots.",
"authors": [{
"fullName": "Alper Sarikaya",
"givenName": "Alper",
"surname": "Sarikaya",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Gleicher",
"givenName": "Michael",
"surname": "Gleicher",
"__typename": "ArticleAuthorType"
}],
"fno": "08017602",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Scatterplots: Tasks, Data, and Designs",
"year": "2018",
"pages": "402-412",
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}, {
"id": "13rRUILtJqV",
"abstract": "Supporting comparison is a common and diverse challenge in visualization. Such support is difficult to design because solutions must address both the specifics of their scenario as well as the general issues of comparison. This paper aids designers by providing a strategy for considering those general issues. It presents four considerations that abstract comparison. These considerations identify issues and categorize solutions in a domain independent manner. The first considers how the common elements of comparison—a target set of items that are related and an action the user wants to perform on that relationship—are present in an analysis problem. The second considers why these elements lead to challenges because of their scale, in number of items, complexity of items, or complexity of relationship. The third considers what strategies address the identified scaling challenges, grouping solutions into three broad categories. The fourth considers which visual designs map to these strategies to provide solutions for a comparison analysis problem. In sequence, these considerations provide a process for developers to consider support for comparison in the design of visualization tools. Case studies show how these considerations can help in the design and evaluation of visualization solutions for comparison problems.",
"authors": [{
"fullName": "Michael Gleicher",
"givenName": "Michael",
"surname": "Gleicher",
"__typename": "ArticleAuthorType"
}],
"fno": "08017615",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Considerations for Visualizing Comparison",
"year": "2018",
"pages": "413-423",
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}, {
"id": "13rRUxAAT0Y",
"abstract": "Mock-ups are rapid, low fidelity prototypes, that are used in many design-related fields to generate and share ideas. While their creation is supported by many mature methods and tools, surprisingly few are suited for the needs of information visualization. In this article, we introduce a novel approach to creating visualizations mock-ups, based on a dialogue between graphic design and parametric toolkit explorations. Our approach consists in iteratively subdividing the display space, while progressively informing each division with realistic data. We show that a wealth of mock-ups can easily be created using only temporary data attributes, as we wait for more realistic data to become available. We describe the implementation of this approach in a D3-based toolkit, which we use to highlight its generative power, and we discuss the potential for transitioning towards higher fidelity prototypes.",
"authors": [{
"fullName": "Romain Vuillemot",
"givenName": "Romain",
"surname": "Vuillemot",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jeremy Boy",
"givenName": "Jeremy",
"surname": "Boy",
"__typename": "ArticleAuthorType"
}],
"fno": "08017650",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Structuring Visualization Mock-Ups at the Graphical Level by Dividing the Display Space",
"year": "2018",
"pages": "424-434",
"pubType": "trans",
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}, {
"id": "13rRUwjGoLL",
"abstract": "Visualization researchers and practitioners engaged in generating or evaluating designs are faced with the difficult problem of transforming the questions asked and actions taken by target users from domain-specific language and context into more abstract forms. Existing abstract task classifications aim to provide support for this endeavour by providing a carefully delineated suite of actions. Our experience is that this bottom-up approach is part of the challenge: low-level actions are difficult to interpret without a higher-level context of analysis goals and the analysis process. To bridge this gap, we propose a framework based on analysis reports derived from open-coding 20 design study papers published at IEEE InfoVis 2009–2015, to build on the previous work of abstractions that collectively encompass a broad variety of domains. The framework is organized in two axes illustrated by nine analysis goals. It helps situate the analysis goals by placing each goal under axes of specificity (Explore, Describe, Explain, Confirm) and number of data populations (Single, Multiple). The single-population types are Discover Observation, Describe Observation, Identify Main Cause, and Collect Evidence. The multiple-population types are Compare Entities, Explain Differences, and Evaluate Hypothesis. Each analysis goal is scoped by an input and an output and is characterized by analysis steps reported in the design study papers. We provide examples of how we and others have used the framework in a top-down approach to abstracting domain problems: visualization designers or researchers first identify the analysis goals of each unit of analysis in an analysis stream, and then encode the individual steps using existing task classifications with the context of the goal, the level of specificity, and the number of populations involved in the analysis.",
"authors": [{
"fullName": "Heidi Lam",
"givenName": "Heidi",
"surname": "Lam",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Melanie Tory",
"givenName": "Melanie",
"surname": "Tory",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tamara Munzner",
"givenName": "Tamara",
"surname": "Munzner",
"__typename": "ArticleAuthorType"
}],
"fno": "08023762",
"isOpenAccess": true,
"issueNum": "01",
"sectionTitle": null,
"title": "Bridging from Goals to Tasks with Design Study Analysis Reports",
"year": "2018",
"pages": "435-445",
"pubType": "trans",
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}, {
"id": "13rRUIM2VH5",
"abstract": "People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively.",
"authors": [{
"fullName": "Jessica Hullman",
"givenName": "Jessica",
"surname": "Hullman",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Matthew Kay",
"givenName": "Matthew",
"surname": "Kay",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yea-Seul Kim",
"givenName": "Yea-Seul",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Samana Shrestha",
"givenName": "Samana",
"surname": "Shrestha",
"__typename": "ArticleAuthorType"
}],
"fno": "08017624",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Imagining Replications: Graphical Prediction & Discrete Visualizations Improve Recall & Estimation of Effect Uncertainty",
"year": "2018",
"pages": "446-456",
"pubType": "trans",
"idPrefix": "tg",
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}, {
"id": "13rRUzp02oy",
"abstract": "We report on a controlled user study comparing three visualization environments for common 3D exploration. Our environments differ in how they exploit natural human perception and interaction capabilities. We compare an augmented-reality head-mounted display (Microsoft HoloLens), a handheld tablet, and a desktop setup. The novel head-mounted HoloLens display projects stereoscopic images of virtual content into a user's real world and allows for interaction in-situ at the spatial position of the 3D hologram. The tablet is able to interact with 3D content through touch, spatial positioning, and tangible markers, however, 3D content is still presented on a 2D surface. Our hypothesis is that visualization environments that match human perceptual and interaction capabilities better to the task at hand improve understanding of 3D visualizations. To better understand the space of display and interaction modalities in visualization environments, we first propose a classification based on three dimensions: perception, interaction, and the spatial and cognitive proximity of the two. Each technique in our study is located at a different position along these three dimensions. We asked 15 participants to perform four tasks, each task having different levels of difficulty for both spatial perception and degrees of freedom for interaction. Our results show that each of the tested environments is more effective for certain tasks, but that generally the desktop environment is still fastest and most precise in almost all cases.",
"authors": [{
"fullName": "Benjamin Bach",
"givenName": "Benjamin",
"surname": "Bach",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ronell Sicat",
"givenName": "Ronell",
"surname": "Sicat",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Johanna Beyer",
"givenName": "Johanna",
"surname": "Beyer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Maxime Cordeil",
"givenName": "Maxime",
"surname": "Cordeil",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hanspeter Pfister",
"givenName": "Hanspeter",
"surname": "Pfister",
"__typename": "ArticleAuthorType"
}],
"fno": "08019876",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "The Hologram in My Hand: How Effective is Interactive Exploration of 3D Visualizations in Immersive Tangible Augmented Reality?",
"year": "2018",
"pages": "457-467",
"pubType": "trans",
"idPrefix": "tg",
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}, {
"id": "13rRUNvyaf7",
"abstract": "Visualizations often appear in multiples, either in a single display (e.g., small multiples, dashboard) or across time or space (e.g., slideshow, set of dashboards). However, existing visualization design guidelines typically focus on single rather than multiple views. Solely following these guidelines can lead to effective yet inconsistent views (e.g., the same field has different axes domains across charts), making interpretation slow and error-prone. Moreover, little is known how consistency balances with other design considerations, making it difficult to incorporate consistency mechanisms in visualization authoring software. We present a wizard-of-oz study in which we observed how Tableau users achieve and sacrifice consistency in an exploration-to-presentation visualization design scenario. We extend (from our prior work) a set of encoding-specific constraints defining consistency across multiple views. Using the constraints as a checklist in our study, we observed cases where participants spontaneously maintained consistent encodings and warned cases where consistency was overlooked. In response to the warnings, participants either revised views for consistency or stated why they thought consistency should be overwritten. We categorize participants' actions and responses as constraint validations and exceptions, depicting the relative importance of consistency and other design considerations under various circumstances (e.g., data cardinality, available encoding resources, chart layout). We discuss automatic consistency checking as a constraint-satisfaction problem and provide design implications for communicating inconsistencies to users.",
"authors": [{
"fullName": "Zening Qu",
"givenName": "Zening",
"surname": "Qu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jessica Hullman",
"givenName": "Jessica",
"surname": "Hullman",
"__typename": "ArticleAuthorType"
}],
"fno": "08017651",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Keeping Multiple Views Consistent: Constraints, Validations, and Exceptions in Visualization Authoring",
"year": "2018",
"pages": "468-477",
"pubType": "trans",
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"__typename": "ArticleType"
}, {
"id": "13rRUxCitJk",
"abstract": "Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a “good” layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.",
"authors": [{
"fullName": "Oh-Hyun Kwon",
"givenName": "Oh-Hyun",
"surname": "Kwon",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tarik Crnovrsanin",
"givenName": "Tarik",
"surname": "Crnovrsanin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kwan-Liu Ma",
"givenName": "Kwan-Liu",
"surname": "Ma",
"__typename": "ArticleAuthorType"
}],
"fno": "08017580",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization",
"year": "2018",
"pages": "478-488",
"pubType": "trans",
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}, {
"id": "13rRUxC0Sw3",
"abstract": "We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes — an approach which previous methods cannot support.",
"authors": [{
"fullName": "Yunhai Wang",
"givenName": "Yunhai",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yanyan Wang",
"givenName": "Yanyan",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yinqi Sun",
"givenName": "Yinqi",
"surname": "Sun",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Lifeng Zhu",
"givenName": "Lifeng",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kecheng Lu",
"givenName": "Kecheng",
"surname": "Lu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Chi-Wing Fu",
"givenName": "Chi-Wing",
"surname": "Fu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Sedlmair",
"givenName": "Michael",
"surname": "Sedlmair",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Oliver Deussen",
"givenName": "Oliver",
"surname": "Deussen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Baoquan Chen",
"givenName": "Baoquan",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}],
"fno": "08017634",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization",
"year": "2018",
"pages": "489-499",
"pubType": "trans",
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}, {
"id": "13rRUyYSWt2",
"abstract": "Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.",
"authors": [{
"fullName": "Christophe Hurter",
"givenName": "Christophe",
"surname": "Hurter",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Stephane Puechmorel",
"givenName": "Stephane",
"surname": "Puechmorel",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Florence Nicol",
"givenName": "Florence",
"surname": "Nicol",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alexandru Telea",
"givenName": "Alexandru",
"surname": "Telea",
"__typename": "ArticleAuthorType"
}],
"fno": "08017623",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Functional Decomposition for Bundled Simplification of Trail Sets",
"year": "2018",
"pages": "500-510",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUx0gefo",
"abstract": "Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques. We hypothesize that truly multimodal interfaces for visualization, those providing users with freedom of expression via both natural language and touch-based direct manipulation input, may provide an effective and engaging user experience. Unfortunately, however, little work has been done in exploring such multimodal visualization interfaces. To address this gap, we have created an architecture and a prototype visualization system called Orko that facilitates both natural language and direct manipulation input. Specifically, Orko focuses on the domain of network visualization, one that has largely relied on WIMP-based interfaces and direct manipulation interaction, and has little or no prior research exploring natural language interaction. We report results from an initial evaluation study of Orko, and use our observations to discuss opportunities and challenges for future work in multimodal network visualization interfaces.",
"authors": [{
"fullName": "Arjun Srinivasan",
"givenName": "Arjun",
"surname": "Srinivasan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "John Stasko",
"givenName": "John",
"surname": "Stasko",
"__typename": "ArticleAuthorType"
}],
"fno": "08019860",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Orko: Facilitating Multimodal Interaction for Visual Exploration and Analysis of Networks",
"year": "2018",
"pages": "511-521",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUy3xY2V",
"abstract": "This paper presents an interactive visualization interface—HiPiler—for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like “snippets”. Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.",
"authors": [{
"fullName": "Fritz Lekschas",
"givenName": "Fritz",
"surname": "Lekschas",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Benjamin Bach",
"givenName": "Benjamin",
"surname": "Bach",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Peter Kerpedjiev",
"givenName": "Peter",
"surname": "Kerpedjiev",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Nils Gehlenborg",
"givenName": "Nils",
"surname": "Gehlenborg",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hanspeter Pfister",
"givenName": "Hanspeter",
"surname": "Pfister",
"__typename": "ArticleAuthorType"
}],
"fno": "08017587",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples",
"year": "2018",
"pages": "522-531",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUEgs2C1",
"abstract": "Multivariate event sequences are ubiquitous: travel history, telecommunication conversations, and server logs are some examples. Besides standard properties such as type and timestamp, events often have other associated multivariate data. Current exploration and analysis methods either focus on the temporal analysis of a single attribute or the structural analysis of the multivariate data only. We present an approach where users can explore event sequences at multivariate and sequential level simultaneously by interactively defining a set of rewrite rules using multivariate regular expressions. Users can store resulting patterns as new types of events or attributes to interactively enrich or simplify event sequences for further investigation. In Eventpad we provide a bottom-up glyph-oriented approach for multivariate event sequence analysis by searching, clustering, and aligning them according to newly defined domain specific properties. We illustrate the effectiveness of our approach with real-world data sets including telecommunication traffic and hospital treatments.",
"authors": [{
"fullName": "Bram C.M. Cappers",
"givenName": "Bram C.M.",
"surname": "Cappers",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jarke J. van Wijk",
"givenName": "Jarke J.",
"surname": "van Wijk",
"__typename": "ArticleAuthorType"
}],
"fno": "08019837",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections",
"year": "2018",
"pages": "532-541",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwdrdSC",
"abstract": "Scatterplot matrices (SPLOMs) are widely used for exploring multidimensional data. Scatterplot diagnostics (scagnostics) approaches measure characteristics of scatterplots to automatically find potentially interesting plots, thereby making SPLOMs more scalable with the dimension count. While statistical measures such as regression lines can capture orientation, and graph-theoretic scagnostics measures can capture shape, there is no scatterplot characterization measure that uses both descriptors. Based on well-known results in shape analysis, we propose a scagnostics approach that captures both scatterplot shape and orientation using skeletons (or medial axes). Our representation can handle complex spatial distributions, helps discovery of principal trends in a multiscale way, scales visually well with the number of samples, is robust to noise, and is automatic and fast to compute. We define skeleton-based similarity metrics for the visual exploration and analysis of SPLOMs. We perform a user study to measure the human perception of scatterplot similarity and compare the outcome to our results as well as to graph-based scagnostics and other visual quality metrics. Our skeleton-based metrics outperform previously defined measures both in terms of closeness to perceptually-based similarity and computation time efficiency.",
"authors": [{
"fullName": "Jose Matute",
"givenName": "Jose",
"surname": "Matute",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alexandru C. Telea",
"givenName": "Alexandru C.",
"surname": "Telea",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Lars Linsen",
"givenName": "Lars",
"surname": "Linsen",
"__typename": "ArticleAuthorType"
}],
"fno": "08017649",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Skeleton-Based Scagnostics",
"year": "2018",
"pages": "542-552",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUzphDy1",
"abstract": "Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.",
"authors": [{
"fullName": "Shusen Liu",
"givenName": "Shusen",
"surname": "Liu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Peer-Timo Bremer",
"givenName": "Peer-Timo",
"surname": "Bremer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jayaraman J. Thiagarajan",
"givenName": "Jayaraman J.",
"surname": "Thiagarajan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Vivek Srikumar",
"givenName": "Vivek",
"surname": "Srikumar",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Bei Wang",
"givenName": "Bei",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yarden Livnat",
"givenName": "Yarden",
"surname": "Livnat",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Valerio Pascucci",
"givenName": "Valerio",
"surname": "Pascucci",
"__typename": "ArticleAuthorType"
}],
"fno": "08019864",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Visual Exploration of Semantic Relationships in Neural Word Embeddings",
"year": "2018",
"pages": "553-562",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUNvyaf6",
"abstract": "Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.",
"authors": [{
"fullName": "Laura E. Matzen",
"givenName": "Laura E.",
"surname": "Matzen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael J. Haass",
"givenName": "Michael J.",
"surname": "Haass",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kristin M. Divis",
"givenName": "Kristin M.",
"surname": "Divis",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Zhiyuan Wang",
"givenName": "Zhiyuan",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Andrew T. Wilson",
"givenName": "Andrew T.",
"surname": "Wilson",
"__typename": "ArticleAuthorType"
}],
"fno": "08017597",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations",
"year": "2018",
"pages": "563-573",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwIF6la",
"abstract": "Effective communication using visualization relies in part on the use of viable encoding strategies. For example, a viewer's ability to rapidly and accurately discern between two or more categorical variables in a chart or figure is contingent upon the distinctiveness of the encodings applied to each variable. Research in perception suggests that color is a more salient visual feature when compared to shape and although that finding is supported by visualization studies, characteristics of shape also yield meaningful differences in distinctiveness. We propose that open or closed shapes (that is, whether shapes are composed of line segments that are bounded across a region of space or not) represent a salient characteristic that influences perceptual processing. Three experiments were performed to test the reliability of the open/closed category; the first two from the perspective of attentional allocation, and the third experiment in the context of multi-class scatterplot displays. In the first, a flanker paradigm was used to test whether perceptual load and open/closed feature category would modulate the effect of the flanker on target processing. Results showed an influence of both variables. The second experiment used a Same/Different reaction time task to replicate and extend those findings. Results from both show that responses are faster and more accurate when closed rather than open shapes are processed as targets, and there is more processing interference when two competing shapes come from the same rather than different open or closed feature categories. The third experiment employed three commonly used visual analytic tasks - perception of average value, numerosity, and linear relationships with both single and dual displays of open and closed symbols. Our findings show that for numerosity and trend judgments, in particular, that different symbols from the same open or closed feature category cause more perceptual interference when they are presented together in a plot than symbols from different categories. Moreover, the extent of the interference appears to depend upon whether the participant is focused on processing open or closed symbols.",
"authors": [{
"fullName": "David Burlinson",
"givenName": "David",
"surname": "Burlinson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kalpathi Subramanian",
"givenName": "Kalpathi",
"surname": "Subramanian",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Paula Goolkasian",
"givenName": "Paula",
"surname": "Goolkasian",
"__typename": "ArticleAuthorType"
}],
"fno": "08019826",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Open vs. Closed Shapes: New Perceptual Categories?",
"year": "2018",
"pages": "574-583",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwbaqLz",
"abstract": "We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that—under certain circumstances—such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.",
"authors": [{
"fullName": "Andre Calero Valdez",
"givenName": "Andre Calero",
"surname": "Valdez",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Martina Ziefle",
"givenName": "Martina",
"surname": "Ziefle",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Sedlmair",
"givenName": "Michael",
"surname": "Sedlmair",
"__typename": "ArticleAuthorType"
}],
"fno": "08022891",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Priming and Anchoring Effects in Visualization",
"year": "2018",
"pages": "584-594",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyueghe",
"abstract": "In this paper, we present story curves, a visualization technique for exploring and communicating nonlinear narratives in movies. A nonlinear narrative is a storytelling device that portrays events of a story out of chronological order, e.g., in reverse order or going back and forth between past and future events. Many acclaimed movies employ unique narrative patterns which in turn have inspired other movies and contributed to the broader analysis of narrative patterns in movies. However, understanding and communicating nonlinear narratives is a difficult task due to complex temporal disruptions in the order of events as well as no explicit records specifying the actual temporal order of the underlying story. Story curves visualize the nonlinear narrative of a movie by showing the order in which events are told in the movie and comparing them to their actual chronological order, resulting in possibly meandering visual patterns in the curve. We also present Story Explorer, an interactive tool that visualizes a story curve together with complementary information such as characters and settings. Story Explorer further provides a script curation interface that allows users to specify the chronological order of events in movies. We used Story Explorer to analyze 10 popular nonlinear movies and describe the spectrum of narrative patterns that we discovered, including some novel patterns not previously described in the literature. Feedback from experts highlights potential use cases in screenplay writing and analysis, education and film production. A controlled user study shows that users with no expertise are able to understand visual patterns of nonlinear narratives using story curves.",
"authors": [{
"fullName": "Nam Wook Kim",
"givenName": "Nam Wook",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Benjamin Bach",
"givenName": "Benjamin",
"surname": "Bach",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hyejin Im",
"givenName": "Hyejin",
"surname": "Im",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Sasha Schriber",
"givenName": "Sasha",
"surname": "Schriber",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Markus Gross",
"givenName": "Markus",
"surname": "Gross",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hanspeter Pfister",
"givenName": "Hanspeter",
"surname": "Pfister",
"__typename": "ArticleAuthorType"
}],
"fno": "08017584",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Visualizing Nonlinear Narratives with Story Curves",
"year": "2018",
"pages": "595-604",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxD9gXN",
"abstract": "We extend the popular brushing and linking technique by incorporating personal agency in the interaction. We map existing research related to brushing and linking into a design space that deconstructs the interaction technique into three components: source (what is being brushed), link (the expression of relationship between source and target), and target (what is revealed as related to the source). Using this design space, we created MyBrush, a unified interface that offers personal agency over brushing and linking by giving people the flexibility to configure the source, link, and target of multiple brushes. The results of three focus groups demonstrate that people with different backgrounds leveraged personal agency in different ways, including performing complex tasks and showing links explicitly. We reflect on these results, paving the way for future research on the role of personal agency in information visualization.",
"authors": [{
"fullName": "Philipp Koytek",
"givenName": "Philipp",
"surname": "Koytek",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Charles Perin",
"givenName": "Charles",
"surname": "Perin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jo Vermeulen",
"givenName": "Jo",
"surname": "Vermeulen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Elisabeth Andre",
"givenName": "Elisabeth",
"surname": "Andre",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Sheelagh Carpendale",
"givenName": "Sheelagh",
"surname": "Carpendale",
"__typename": "ArticleAuthorType"
}],
"fno": "08017621",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "MyBrush: Brushing and Linking with Personal Agency",
"year": "2018",
"pages": "605-615",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUNvgz4o",
"abstract": "Conventional dot plots use a constant dot size and are typically applied to show the frequency distribution of small data sets. Unfortunately, they are not designed for a high dynamic range of frequencies. We address this problem by introducing nonlinear dot plots. Adopting the idea of nonlinear scaling from logarithmic bar charts, our plots allow for dots of varying size so that columns with a large number of samples are reduced in height. For the construction of these diagrams, we introduce an efficient two-way sweep algorithm that leads to a dense and symmetrical layout. We compensate aliasing artifacts at high dot densities by a specifically designed low-pass filtering method. Examples of nonlinear dot plots are compared to conventional dot plots as well as linear and logarithmic histograms. Finally, we include feedback from an expert review.",
"authors": [{
"fullName": "Nils Rodrigues",
"givenName": "Nils",
"surname": "Rodrigues",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Weiskopf",
"givenName": "Daniel",
"surname": "Weiskopf",
"__typename": "ArticleAuthorType"
}],
"fno": "08017644",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Nonlinear Dot Plots",
"year": "2018",
"pages": "616-625",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwbaqUT",
"abstract": "We present VisTiles, a conceptual framework that uses a set of mobile devices to distribute and coordinate visualization views for the exploration of multivariate data. In contrast to desktop-based interfaces for information visualization, mobile devices offer the potential to provide a dynamic and user-defined interface supporting co-located collaborative data exploration with different individual workflows. As part of our framework, we contribute concepts that enable users to interact with coordinated & multiple views (CMV) that are distributed across several mobile devices. The major components of the framework are: (i) dynamic and flexible layouts for CMV focusing on the distribution of views and (ii) an interaction concept for smart adaptations and combinations of visualizations utilizing explicit side-by-side arrangements of devices. As a result, users can benefit from the possibility to combine devices and organize them in meaningful spatial layouts. Furthermore, we present a web-based prototype implementation as a specific instance of our concepts. This implementation provides a practical application case enabling users to explore a multivariate data collection. We also illustrate the design process including feedback from a preliminary user study, which informed the design of both the concepts and the final prototype.",
"authors": [{
"fullName": "Ricardo Langner",
"givenName": "Ricardo",
"surname": "Langner",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tom Horak",
"givenName": "Tom",
"surname": "Horak",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Raimund Dachselt",
"givenName": "Raimund",
"surname": "Dachselt",
"__typename": "ArticleAuthorType"
}],
"fno": "08017609",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "VisTiles: Coordinating and Combining Co-located Mobile Devices for Visual Data Exploration",
"year": "2018",
"pages": "626-636",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxYIN4e",
"abstract": "Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.",
"authors": [{
"fullName": "Jorge Poco",
"givenName": "Jorge",
"surname": "Poco",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Angela Mayhua",
"givenName": "Angela",
"surname": "Mayhua",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jeffrey Heer",
"givenName": "Jeffrey",
"surname": "Heer",
"__typename": "ArticleAuthorType"
}],
"fno": "08017646",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations",
"year": "2018",
"pages": "637-646",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUIJuxvp",
"abstract": "We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood-aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state-of-the-art methods and show that we can improve user performance, user satisfaction, as well as the layout itself.",
"authors": [{
"fullName": "Yunhai Wang",
"givenName": "Yunhai",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xiaowei Chu",
"givenName": "Xiaowei",
"surname": "Chu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Chen Bao",
"givenName": "Chen",
"surname": "Bao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Lifeng Zhu",
"givenName": "Lifeng",
"surname": "Zhu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Oliver Deussen",
"givenName": "Oliver",
"surname": "Deussen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Baoquan Chen",
"givenName": "Baoquan",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Sedlmair",
"givenName": "Michael",
"surname": "Sedlmair",
"__typename": "ArticleAuthorType"
}],
"fno": "08017586",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "EdWordle: Consistency-Preserving Word Cloud Editing",
"year": "2018",
"pages": "647-656",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwkfAZl",
"abstract": "In this paper we present a set of four user studies aimed at exploring the visual design space of what we call keyword summaries: lists of words with associated quantitative values used to help people derive an intuition of what information a given document collection (or part of it) may contain. We seek to systematically study how different visual representations may affect people's performance in extracting information out of keyword summaries. To this purpose, we first create a design space of possible visual representations and compare the possible solutions in this design space through a variety of representative tasks and performance metrics. Other researchers have, in the past, studied some aspects of effectiveness with word clouds, however, the existing literature is somewhat scattered and do not seem to address the problem in a sufficiently systematic and holistic manner. The results of our studies showed a strong dependency on the tasks users are performing. In this paper we present details of our methodology, the results, as well as, guidelines on how to design effective keyword summaries based in our discoveries.",
"authors": [{
"fullName": "Cristian Felix",
"givenName": "Cristian",
"surname": "Felix",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Steven Franconeri",
"givenName": "Steven",
"surname": "Franconeri",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Enrico Bertini",
"givenName": "Enrico",
"surname": "Bertini",
"__typename": "ArticleAuthorType"
}],
"fno": "08017641",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Taking Word Clouds Apart: An Empirical Investigation of the Design Space for Keyword Summaries",
"year": "2018",
"pages": "657-666",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUygBw7f",
"abstract": "Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVis, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks. Long-term usage data after putting the tool online revealed great interest in the machine learning community.",
"authors": [{
"fullName": "Hendrik Strobelt",
"givenName": "Hendrik",
"surname": "Strobelt",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Sebastian Gehrmann",
"givenName": "Sebastian",
"surname": "Gehrmann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hanspeter Pfister",
"givenName": "Hanspeter",
"surname": "Pfister",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alexander M. Rush",
"givenName": "Alexander M.",
"surname": "Rush",
"__typename": "ArticleAuthorType"
}],
"fno": "08017583",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks",
"year": "2018",
"pages": "667-676",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxCitJl",
"abstract": "Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace “diff” tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selected tables. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios.",
"authors": [{
"fullName": "Christina Niederer",
"givenName": "Christina",
"surname": "Niederer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Holger Stitz",
"givenName": "Holger",
"surname": "Stitz",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Reem Hourieh",
"givenName": "Reem",
"surname": "Hourieh",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Florian Grassinger",
"givenName": "Florian",
"surname": "Grassinger",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Wolfgang Aigner",
"givenName": "Wolfgang",
"surname": "Aigner",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Marc Streit",
"givenName": "Marc",
"surname": "Streit",
"__typename": "ArticleAuthorType"
}],
"fno": "08017626",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "TACO: Visualizing Changes in Tables Over Time",
"year": "2018",
"pages": "677-686",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxlgxTr",
"abstract": "Building Information Modeling (BIM) provides an integrated 3D environment to manage large-scale engineering projects. The Architecture, Engineering and Construction (AEC) industry explores 4D visualizations over these datasets for virtual construction planning. However, existing solutions lack adequate visual mechanisms to inspect the underlying schedule and make inconsistencies readily apparent. The goal of this paper is to apply best practices of information visualization to improve 4D analysis of construction plans. We first present a review of previous work that identifies common use cases and limitations. We then consulted with AEC professionals to specify the main design requirements for such applications. These guided the development of CasCADe, a novel 4D visualization system where task sequencing and spatio-temporal simultaneity are immediately apparent. This unique framework enables the combination of diverse analytical features to create an information-rich analysis environment. We also describe how engineering collaborators used CasCADe to review the real-world construction plans of an Oil & Gas process plant. The system made evident schedule uncertainties, identified work-space conflicts and helped analyze other constructability issues. The results and contributions of this paper suggest new avenues for future research in information visualization for the AEC industry.",
"authors": [{
"fullName": "Paulo Ivson",
"givenName": "Paulo",
"surname": "Ivson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Nascimento",
"givenName": "Daniel",
"surname": "Nascimento",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Waldemar Celes",
"givenName": "Waldemar",
"surname": "Celes",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Simone DJ Barbosa",
"givenName": "Simone DJ",
"surname": "Barbosa",
"__typename": "ArticleAuthorType"
}],
"fno": "08019847",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "CasCADe: A Novel 4D Visualization System for Virtual Construction Planning",
"year": "2018",
"pages": "687-697",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUx0xPTV",
"abstract": "We empirically evaluate the extent to which people perceive non-constant time and speed encoded on 2D paths. In our graphical perception study, we evaluate nine encodings from the literature for both straight and curved paths. Visualizing time and speed information is a challenge when the x and y axes already encode other data dimensions, for example when plotting a trip on a map. This is particularly true in disciplines such as time-geography and movement analytics that often require visualizing spatio-temporal trajectories. A common approach is to use 2D+time trajectories, which are 2D paths for which time is an additional dimension. However, there are currently no guidelines regarding how to represent time and speed on such paths. Our study results provide InfoVis designers with clear guidance regarding which encodings to use and which ones to avoid; in particular, we suggest using color value to encode speed and segment length to encode time whenever possible.",
"authors": [{
"fullName": "Charles Perin",
"givenName": "Charles",
"surname": "Perin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tiffany Wun",
"givenName": "Tiffany",
"surname": "Wun",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Richard Pusch",
"givenName": "Richard",
"surname": "Pusch",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Sheelagh Carpendale",
"givenName": "Sheelagh",
"surname": "Carpendale",
"__typename": "ArticleAuthorType"
}],
"fno": "08017638",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Assessing the Graphical Perception of Time and Speed on 2D+Time Trajectories",
"year": "2018",
"pages": "698-708",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyY28YD",
"abstract": "The rapid development of information technology paved the way for the recording of fine-grained data, such as stroke techniques and stroke placements, during a table tennis match. This data recording creates opportunities to analyze and evaluate matches from new perspectives. Nevertheless, the increasingly complex data poses a significant challenge to make sense of and gain insights into. Analysts usually employ tedious and cumbersome methods which are limited to watching videos and reading statistical tables. However, existing sports visualization methods cannot be applied to visualizing table tennis competitions due to different competition rules and particular data attributes. In this work, we collaborate with data analysts to understand and characterize the sophisticated domain problem of analysis of table tennis data. We propose iTTVis, a novel interactive table tennis visualization system, which to our knowledge, is the first visual analysis system for analyzing and exploring table tennis data. iTTVis provides a holistic visualization of an entire match from three main perspectives, namely, time-oriented, statistical, and tactical analyses. The proposed system with several well-coordinated views not only supports correlation identification through statistics and pattern detection of tactics with a score timeline but also allows cross analysis to gain insights. Data analysts have obtained several new insights by using iTTVis. The effectiveness and usability of the proposed system are demonstrated with four case studies.",
"authors": [{
"fullName": "Yingcai Wu",
"givenName": "Yingcai",
"surname": "Wu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ji Lan",
"givenName": "Ji",
"surname": "Lan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Xinhuan Shu",
"givenName": "Xinhuan",
"surname": "Shu",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Chenyang Ji",
"givenName": "Chenyang",
"surname": "Ji",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kejian Zhao",
"givenName": "Kejian",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jiachen Wang",
"givenName": "Jiachen",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hui Zhang",
"givenName": "Hui",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
}],
"fno": "08017600",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "iTTVis: Interactive Visualization of Table Tennis Data",
"year": "2018",
"pages": "709-718",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyuvRoQ",
"abstract": "We present a novel type of circular treemap, where we intentionally allocate extra space for additional visual variables. With this extended visual design space, we encode hierarchically structured data along with their uncertainties in a combined diagram. We introduce a hierarchical and force-based circle-packing algorithm to compute Bubble Treemaps, where each node is visualized using nested contour arcs. Bubble Treemaps do not require any color or shading, which offers additional design choices. We explore uncertainty visualization as an application of our treemaps using standard error and Monte Carlo-based statistical models. To this end, we discuss how uncertainty propagates within hierarchies. Furthermore, we show the effectiveness of our visualization using three different examples: the package structure of Flare, the S&P 500 index, and the US consumer expenditure survey.",
"authors": [{
"fullName": "Jochen Gortler",
"givenName": "Jochen",
"surname": "Gortler",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Christoph Schulz",
"givenName": "Christoph",
"surname": "Schulz",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Weiskopf",
"givenName": "Daniel",
"surname": "Weiskopf",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Oliver Deussen",
"givenName": "Oliver",
"surname": "Deussen",
"__typename": "ArticleAuthorType"
}],
"fno": "08017613",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Bubble Treemaps for Uncertainty Visualization",
"year": "2018",
"pages": "719-728",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUIJcWlt",
"abstract": "Treemaps are a popular tool to visualize hierarchical data: items are represented by nested rectangles and the area of each rectangle corresponds to the data being visualized for this item. The visual quality of a treemap is commonly measured via the aspect ratio of the rectangles. If the data changes, then a second important quality criterion is the stability of the treemap: how much does the treemap change as the data changes. We present a novel stable treemapping algorithm that has very high visual quality. Whereas existing treemapping algorithms generally recompute the treemap every time the input changes, our algorithm changes the layout of the treemap using only local modifications. This approach not only gives us direct control over stability, but it also allows us to use a larger set of possible layouts, thus provably resulting in treemaps of higher visual quality compared to existing algorithms. We further prove that we can reach all possible treemap layouts using only our local modifications. Furthermore, we introduce a new measure for stability that better captures the relative positions of rectangles. We finally show via experiments on real-world data that our algorithm outperforms existing treemapping algorithms also in practice on either visual quality and/or stability. Our algorithm scores high on stability regardless of whether we use an existing stability measure or our new measure.",
"authors": [{
"fullName": "Max Sondag",
"givenName": "Max",
"surname": "Sondag",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Bettina Speckmann",
"givenName": "Bettina",
"surname": "Speckmann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Kevin Verbeek",
"givenName": "Kevin",
"surname": "Verbeek",
"__typename": "ArticleAuthorType"
}],
"fno": "08019841",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Stable Treemaps via Local Moves",
"year": "2018",
"pages": "729-738",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUytWF9r",
"abstract": "Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work, we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.",
"authors": [{
"fullName": "Thomas Hollt",
"givenName": "Thomas",
"surname": "Hollt",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Nicola Pezzotti",
"givenName": "Nicola",
"surname": "Pezzotti",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Vincent van Unen",
"givenName": "Vincent",
"surname": "van Unen",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Frits Koning",
"givenName": "Frits",
"surname": "Koning",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Boudewijn P.F. Lelieveldt",
"givenName": "Boudewijn P.F.",
"surname": "Lelieveldt",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Anna Vilanova",
"givenName": "Anna",
"surname": "Vilanova",
"__typename": "ArticleAuthorType"
}],
"fno": "08017575",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis",
"year": "2018",
"pages": "739-748",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUB6Sq0E",
"abstract": "We explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support.",
"authors": [{
"fullName": "Evanthia Dimara",
"givenName": "Evanthia",
"surname": "Dimara",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Anastasia Bezerianos",
"givenName": "Anastasia",
"surname": "Bezerianos",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Pierre Dragicevic",
"givenName": "Pierre",
"surname": "Dragicevic",
"__typename": "ArticleAuthorType"
}],
"fno": "08019855",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support",
"year": "2018",
"pages": "749-759",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUIJcWls",
"abstract": "In addition to visualizing input data, interactive visualizations have the potential to be social artifacts that reveal other people's perspectives on the data. However, how such social information embedded in a visualization impacts a viewer's interpretation of the data remains unknown. Inspired by recent interactive visualizations that display people's expectations of data against the data, we conducted a controlled experiment to evaluate the effect of showing social information in the form of other people's expectations on people's ability to recall the data, the degree to which they adjust their expectations to align with the data, and their trust in the accuracy of the data. We found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone. Additionally, participants trusted the accuracy of the data less and were more likely to maintain their initial expectations when other people's expectations aligned with their own initial expectations but not with the data. We conclude by characterizing the design space for visualizing others' expectations alongside data.",
"authors": [{
"fullName": "Yea-Seul Kim",
"givenName": "Yea-Seul",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Katharina Reinecke",
"givenName": "Katharina",
"surname": "Reinecke",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jessica Hullman",
"givenName": "Jessica",
"surname": "Hullman",
"__typename": "ArticleAuthorType"
}],
"fno": "08019830",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation",
"year": "2018",
"pages": "760-769",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyYSWl5",
"abstract": "We investigate whether the notion of active reading for text might be usefully applied to visualizations. Through a qualitative study we explored whether people apply observable active reading techniques when reading paper-based node-link visualizations. Participants used a range of physical actions while reading, and from these we synthesized an initial set of active reading techniques for visualizations. To learn more about the potential impact such techniques may have on visualization reading, we implemented support for one type of physical action from our observations (making freeform marks) in an interactive node-link visualization. Results from our quantitative study of this implementation show that interactive support for active reading techniques can improve the accuracy of performing low-level visualization tasks. Together, our studies suggest that the active reading space is ripe for research exploration within visualization and can lead to new interactions that make for a more flexible and effective visualization reading experience.",
"authors": [{
"fullName": "Jagoda Walny",
"givenName": "Jagoda",
"surname": "Walny",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Samuel Huron",
"givenName": "Samuel",
"surname": "Huron",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Charles Perin",
"givenName": "Charles",
"surname": "Perin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tiffany Wun",
"givenName": "Tiffany",
"surname": "Wun",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Richard Pusch",
"givenName": "Richard",
"surname": "Pusch",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Sheelagh Carpendale",
"givenName": "Sheelagh",
"surname": "Carpendale",
"__typename": "ArticleAuthorType"
}],
"fno": "08017606",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Active Reading of Visualizations",
"year": "2018",
"pages": "770-780",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxAAT7J",
"abstract": "We provide a reappraisal of Tal and Wansink's study “Blinded with Science”, where seemingly trivial charts were shown to increase belief in drug efficacy, presumably because charts are associated with science. Through a series of four replications conducted on two crowdsourcing platforms, we investigate an alternative explanation, namely, that the charts allowed participants to better assess the drug's efficacy. Considered together, our experiments suggest that the chart seems to have indeed promoted understanding, although the effect is likely very small. Meanwhile, we were unable to replicate the original study's findings, as text with chart appeared to be no more persuasive - and sometimes less persuasive - than text alone. This suggests that the effect may not be as robust as claimed and may need specific conditions to be reproduced. Regardless, within our experimental settings and considering our study as a whole ($\\mathrm{N}=623$), the chart's contribution to understanding was clearly larger than its contribution to persuasion.",
"authors": [{
"fullName": "Pierre Dragicevic",
"givenName": "Pierre",
"surname": "Dragicevic",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yvonne Jansen",
"givenName": "Yvonne",
"surname": "Jansen",
"__typename": "ArticleAuthorType"
}],
"fno": "08017611",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Blinded with Science or Informed by Charts? A Replication Study",
"year": "2018",
"pages": "781-790",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUyY28YC",
"abstract": "Visualizations are nowadays appearing in popular media and are used everyday in the workplace. This democratisation of visualization challenges educators to develop effective learning strategies, in order to train the next generation of creative visualization specialists. There is high demand for skilled individuals who can analyse a problem, consider alternative designs, develop new visualizations, and be creative and innovative. Our three-stage framework, leads the learner through a series of tasks, each designed to develop different skills necessary for coming up with creative, innovative, effective, and purposeful visualizations. For that, we get the learners to create an explanatory visualization of an algorithm of their choice. By making an algorithm choice, and by following an active-learning and project-based strategy, the learners take ownership of a particular visualization challenge. They become enthusiastic to develop good results and learn different creative skills on their learning journey.",
"authors": [{
"fullName": "Jonathan C. Roberts",
"givenName": "Jonathan C.",
"surname": "Roberts",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Panagiotis D. Ritsos",
"givenName": "Panagiotis D.",
"surname": "Ritsos",
"__typename": "ArticleAuthorType"
}, {
"fullName": "James R. Jackson",
"givenName": "James R.",
"surname": "Jackson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Christopher Headleand",
"givenName": "Christopher",
"surname": "Headleand",
"__typename": "ArticleAuthorType"
}],
"fno": "08017594",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "The Explanatory Visualization Framework: An Active Learning Framework for Teaching Creative Computing Using Explanatory Visualizations",
"year": "2018",
"pages": "791-801",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUB7a116",
"abstract": "Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes, such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters, interactive touch tables, and virtual reality headsets.",
"authors": [{
"fullName": "Karl Bladin",
"givenName": "Karl",
"surname": "Bladin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Emil Axelsson",
"givenName": "Emil",
"surname": "Axelsson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Erik Broberg",
"givenName": "Erik",
"surname": "Broberg",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Carter Emmart",
"givenName": "Carter",
"surname": "Emmart",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Patric Ljung",
"givenName": "Patric",
"surname": "Ljung",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alexander Bock",
"givenName": "Alexander",
"surname": "Bock",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Anders Ynnerman",
"givenName": "Anders",
"surname": "Ynnerman",
"__typename": "ArticleAuthorType"
}],
"fno": "08017579",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Globe Browsing: Contextualized Spatio-Temporal Planetary Surface Visualization",
"year": "2018",
"pages": "802-811",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUILLkvx",
"abstract": "We present TopoAngler, a visualization framework that enables an interactive user-guided segmentation of fishes contained in a micro-CT scan. The inherent noise in the CT scan coupled with the often disconnected (and sometimes broken) skeletal structure of fishes makes an automatic segmentation of the volume impractical. To overcome this, our framework combines techniques from computational topology with an interactive visual interface, enabling the human-in-the-Ioop to effectively extract fishes from the volume. In the first step, the join tree of the input is used to create a hierarchical segmentation of the volume. Through the use of linked views, the visual interface then allows users to interactively explore this hierarchy, and gather parts of individual fishes into a coherent sub-volume, thus reconstructing entire fishes. Our framework was primarily developed for its application to CT scans of fishes, generated as part of the ScanAllFish project, through close collaboration with their lead scientist. However, we expect it to also be applicable in other biological applications where a single dataset contains multiple specimen; a common routine that is now widely followed in laboratories to increase throughput of expensive CT scanners.",
"authors": [{
"fullName": "Alexander Bock",
"givenName": "Alexander",
"surname": "Bock",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Harish Doraiswamy",
"givenName": "Harish",
"surname": "Doraiswamy",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Adam Summers",
"givenName": "Adam",
"surname": "Summers",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Claudio Silva",
"givenName": "Claudio",
"surname": "Silva",
"__typename": "ArticleAuthorType"
}],
"fno": "08017639",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "TopoAngler: Interactive Topology-Based Extraction of Fishes",
"year": "2018",
"pages": "812-821",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwd9CG8",
"abstract": "Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types.",
"authors": [{
"fullName": "Bastian Rieck",
"givenName": "Bastian",
"surname": "Rieck",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ulderico Fugacci",
"givenName": "Ulderico",
"surname": "Fugacci",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jonas Lukasczyk",
"givenName": "Jonas",
"surname": "Lukasczyk",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Heike Leitte",
"givenName": "Heike",
"surname": "Leitte",
"__typename": "ArticleAuthorType"
}],
"fno": "08017588",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks",
"year": "2018",
"pages": "822-831",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxZzAhJ",
"abstract": "This system paper presents the Topology ToolKit (TTK), a software platform designed for the topological analysis of scalar data in scientific visualization. While topological data analysis has gained in popularity over the last two decades, it has not yet been widely adopted as a standard data analysis tool for end users or developers. TTK aims at addressing this problem by providing a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependency-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code. online documentation and video tutorials are available on TTK's website [108].",
"authors": [{
"fullName": "Julien Tierny",
"givenName": "Julien",
"surname": "Tierny",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Guillaume Favelier",
"givenName": "Guillaume",
"surname": "Favelier",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Joshua A. Levine",
"givenName": "Joshua A.",
"surname": "Levine",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Charles Gueunet",
"givenName": "Charles",
"surname": "Gueunet",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Michael Michaux",
"givenName": "Michael",
"surname": "Michaux",
"__typename": "ArticleAuthorType"
}],
"fno": "08017627",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "The Topology ToolKit",
"year": "2018",
"pages": "832-842",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUzphDy0",
"abstract": "Branched covering spaces are a mathematical concept which originates from complex analysis and topology and has applications in tensor field topology and geometry remeshing. Given a manifold surface and an $N$-way rotational symmetry field, a branched covering space is a manifold surface that has an $N$-to-1 map to the original surface except at the ramification points, which correspond to the singularities in the rotational symmetry field. Understanding the notion and mathematical properties of branched covering spaces is important to researchers in tensor field visualization and geometry processing, and their application areas. In this paper, we provide a framework to interactively design and visualize the branched covering space (BCS) of an input mesh surface and a rotational symmetry field defined on it. In our framework, the user can visualize not only the BCSs but also their construction process. In addition, our system allows the user to design the geometric realization of the BCS using mesh deformation techniques as well as connecting tubes. This enables the user to verify important facts about BCSs such as that they are manifold surfaces around singularities, as well as the Riemann-Hurwitz formula which relates the Euler characteristic of the BCS to that of the original mesh. Our system is evaluated by student researchers in scientific visualization and geometry processing as well as faculty members in mathematics at our university who teach topology. We include their evaluations and feedback in the paper.",
"authors": [{
"fullName": "Lawrence Roy",
"givenName": "Lawrence",
"surname": "Roy",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Prashant Kumar",
"givenName": "Prashant",
"surname": "Kumar",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Sanaz Golbabaei",
"givenName": "Sanaz",
"surname": "Golbabaei",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Yue Zhang",
"givenName": "Yue",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Eugene Zhang",
"givenName": "Eugene",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
}],
"fno": "08017576",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Interactive Design and Visualization of Branched Covering Spaces",
"year": "2018",
"pages": "843-852",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwI5U7Y",
"abstract": "This paper presents Abstractocyte, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. The study of astrocytes has immense potential for understanding brain function. However, their complex and widely-branching structure requires high-resolution electron microscopy imaging and makes visualization and analysis challenging. Furthermore, the structure and function of astrocytes is very different from neurons, and therefore requires the development of new visualization and analysis tools. With Abstractocyte, biologists can explore the morphology of astrocytes using various visual abstraction levels, while simultaneously analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing astrocytes and neurons. Neuroscientists can choose a specific joint visualization as a point in this space. Interactively moving this point allows them to smoothly transition between different abstraction levels in an intuitive manner. In contrast to simply switching between different visualizations, this preserves the visual context and correlations throughout the transition. Users can smoothly navigate from concrete, highly-detailed 3D views to simplified and abstracted 2D views. In addition to investigating astrocytes, neurons, and their relationships, we enable the interactive analysis of the distribution of glycogen, which is of high importance to neuroscientists. We describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system to assess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage.",
"authors": [{
"fullName": "Haneen Mohammed",
"givenName": "Haneen",
"surname": "Mohammed",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ali K. Al-Awami",
"givenName": "Ali K.",
"surname": "Al-Awami",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Johanna Beyer",
"givenName": "Johanna",
"surname": "Beyer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Corrado Cali",
"givenName": "Corrado",
"surname": "Cali",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Pierre Magistretti",
"givenName": "Pierre",
"surname": "Magistretti",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Hanspeter Pfister",
"givenName": "Hanspeter",
"surname": "Pfister",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Markus Hadwiger",
"givenName": "Markus",
"surname": "Hadwiger",
"__typename": "ArticleAuthorType"
}],
"fno": "08017643",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Abstractocyte: A Visual Tool for Exploring Nanoscale Astroglial Cells",
"year": "2018",
"pages": "853-861",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwcAqqm",
"abstract": "We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.",
"authors": [{
"fullName": "Tobias Klein",
"givenName": "Tobias",
"surname": "Klein",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ludovic Autin",
"givenName": "Ludovic",
"surname": "Autin",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Barbora Kozlikova",
"givenName": "Barbora",
"surname": "Kozlikova",
"__typename": "ArticleAuthorType"
}, {
"fullName": "David S. Goodsell",
"givenName": "David S.",
"surname": "Goodsell",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Arthur Olson",
"givenName": "Arthur",
"surname": "Olson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "M. Eduard Groller",
"givenName": "M. Eduard",
"surname": "Groller",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ivan Viola",
"givenName": "Ivan",
"surname": "Viola",
"__typename": "ArticleAuthorType"
}],
"fno": "08019859",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Instant Construction and Visualization of Crowded Biological Environments",
"year": "2018",
"pages": "862-872",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxNmPDV",
"abstract": "High-resolution manometry is an imaging modality which enables the categorization of esophageal motility disorders. Spatio-temporal pressure data along the esophagus is acquired using a tubular device and multiple test swallows are performed by the patient. Current approaches visualize these swallows as individual instances, despite the fact that aggregated metrics are relevant in the diagnostic process. Based on the current Chicago Classification, which serves as the gold standard in this area, we introduce a visualization supporting an efficient and correct diagnosis. To reach this goal, we propose a novel decision graph representing the Chicago Classification with workflow optimization in mind. Based on this graph, we are further able to prioritize the different metrics used during diagnosis and can exploit this prioritization in the actual data visualization. Thus, different disorders and their related parameters are directly represented and intuitively influence the appearance of our visualization. Within this paper, we introduce our novel visualization, justify the design decisions, and provide the results of a user study we performed with medical students as well as a domain expert. On top of the presented visualization, we further discuss how to derive a visual signature for individual patients that allows us for the first time to perform an intuitive comparison between subjects, in the form of small multiples.",
"authors": [{
"fullName": "Julian Kreiser",
"givenName": "Julian",
"surname": "Kreiser",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Alexander Hann",
"givenName": "Alexander",
"surname": "Hann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Eugen Zizer",
"givenName": "Eugen",
"surname": "Zizer",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Timo Ropinski",
"givenName": "Timo",
"surname": "Ropinski",
"__typename": "ArticleAuthorType"
}],
"fno": "08017642",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Decision Graph Embedding for High-Resolution Manometry Diagnosis",
"year": "2018",
"pages": "873-882",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUILtJmf",
"abstract": "We propose a system to facilitate biology communication by developing a pipeline to support the instructional visualization of heterogeneous biological data on heterogeneous user-devices. Discoveries and concepts in biology are typically summarized with illustrations assembled manually from the interpretation and application of heterogenous data. The creation of such illustrations is time consuming, which makes it incompatible with frequent updates to the measured data as new discoveries are made. Illustrations are typically non-interactive, and when an illustration is updated, it still has to reach the user. Our system is designed to overcome these three obstacles. It supports the integration of heterogeneous datasets, reflecting the knowledge that is gained from different data sources in biology. After pre-processing the datasets, the system transforms them into visual representations as inspired by scientific illustrations. As opposed to traditional scientific illustration these representations are generated in real-time - they are interactive. The code generating the visualizations can be embedded in various software environments. To demonstrate this, we implemented both a desktop application and a remote-rendering server in which the pipeline is embedded. The remote-rendering server supports multi-threaded rendering and it is able to handle multiple users simultaneously. This scalability to different hardware environments, including multi-GPU setups, makes our system useful for efficient public dissemination of biological discoveries.",
"authors": [{
"fullName": "Peter Mindek",
"givenName": "Peter",
"surname": "Mindek",
"__typename": "ArticleAuthorType"
}, {
"fullName": "David Kouril",
"givenName": "David",
"surname": "Kouril",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Johannes Sorger",
"givenName": "Johannes",
"surname": "Sorger",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Daniel Toloudis",
"givenName": "Daniel",
"surname": "Toloudis",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Blair Lyons",
"givenName": "Blair",
"surname": "Lyons",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Graham Johnson",
"givenName": "Graham",
"surname": "Johnson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "M. Eduard Groller",
"givenName": "M. Eduard",
"surname": "Groller",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Ivan Viola",
"givenName": "Ivan",
"surname": "Viola",
"__typename": "ArticleAuthorType"
}],
"fno": "08017635",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Visualization Multi-Pipeline for Communicating Biology",
"year": "2018",
"pages": "883-892",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwInvl6",
"abstract": "Jet-streams, their core lines and their role in atmospheric dynamics have been subject to considerable meteorological research since the first half of the twentieth century. Yet, until today no consistent automated feature detection approach has been proposed to identify jet-stream core lines from 3D wind fields. Such 3D core lines can facilitate meteorological analyses previously not possible. Although jet-stream cores can be manually analyzed by meteorologists in 2D as height ridges in the wind speed field, to the best of our knowledge no automated ridge detection approach has been applied to jet-stream core detection. In this work, we -a team of visualization scientists and meteorologists-propose a method that exploits directional information in the wind field to extract core lines in a robust and numerically less involved manner than traditional 3D ridge detection. For the first time, we apply the extracted 3D core lines to meteorological analysis, considering real-world case studies and demonstrating our method's benefits for weather forecasting and meteorological research.",
"authors": [{
"fullName": "Michael Kern",
"givenName": "Michael",
"surname": "Kern",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Tim Hewson",
"givenName": "Tim",
"surname": "Hewson",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Filip Sadlo",
"givenName": "Filip",
"surname": "Sadlo",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Rudiger Westermann",
"givenName": "Rudiger",
"surname": "Westermann",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Marc Rautenhaus",
"givenName": "Marc",
"surname": "Rautenhaus",
"__typename": "ArticleAuthorType"
}],
"fno": "08017585",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "Robust Detection and Visualization of Jet-Stream Core Lines in Atmospheric Flow",
"year": "2018",
"pages": "893-902",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUxBa5s4",
"abstract": "As the finite element method (FEM) and the finite volume method (FVM), both traditional and high-order variants, continue their proliferation into various applied engineering disciplines, it is important that the visualization techniques and corresponding data analysis tools that act on the results produced by these methods faithfully represent the underlying data. To state this in another way: the interpretation of data generated by simulation needs to be consistent with the numerical schemes that underpin the specific solver technology. As the verifiable visualization literature has demonstrated: visual artifacts produced by the introduction of either explicit or implicit data transformations, such as data resampling, can sometimes distort or even obfuscate key scientific features in the data. In this paper, we focus on the handling of elemental continuity, which is often only $C^{0}$ continuous or piecewise discontinuous, when visualizing primary or derived fields from FEM or FVM simulations. We demonstrate that traditional data handling and visualization of these fields introduce visual errors. In addition, we show how the use of the recently proposed line-SIAC filter provides a way of handling elemental continuity issues in an accuracy-conserving manner with the added benefit of casting the data in a smooth context even if the representation is element discontinuous.",
"authors": [{
"fullName": "Ashok Jallepalli",
"givenName": "Ashok",
"surname": "Jallepalli",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Julia Docampo-Sanchez",
"givenName": "Julia",
"surname": "Docampo-Sanchez",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Jennifer K. Ryan",
"givenName": "Jennifer K.",
"surname": "Ryan",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Robert Haimes",
"givenName": "Robert",
"surname": "Haimes",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Robert M. Kirby",
"givenName": "Robert M.",
"surname": "Kirby",
"__typename": "ArticleAuthorType"
}],
"fno": "08017592",
"isOpenAccess": false,
"issueNum": "01",
"sectionTitle": null,
"title": "On the Treatment of Field Quantities and Elemental Continuity in FEM Solutions",
"year": "2018",
"pages": "903-912",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUwhHcQX",
"abstract": "Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage—and its evaluation—of existing, higher-level visualization design models. In a departure from existing visualization design models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature.",
"authors": [{
"fullName": "G. Elisabeta Marai",
"givenName": "G. Elisabeta",
"surname": "Marai",
"__typename": "ArticleAuthorType"
}],
"fno": "08017610",
"isOpenAccess": true,
"issueNum": "01",
"sectionTitle": null,
"title": "Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization",
"year": "2018",
"pages": "913-922",
"pubType": "trans",
"idPrefix": "tg",
"__typename": "ArticleType"
}, {
"id": "13rRUNvgz9W",
"abstract": "A myriad of design rules for what constitutes a “good” colormap can be found in the literature. Some common rules include order, uniformity, and high discriminative power. However, the meaning of many of these terms is often ambiguous or open to interpretation. At times, different authors may use the same term to describe different concepts or the same rule is described by varying nomenclature. These ambiguities stand in the way of collaborative work, the design of experiments to assess the characteristics of colormaps, and automated colormap generation. In this paper, we review current and historical guidelines for colormap design. We propose a specified taxonomy and provide unambiguous mathematical definitions for the most common design rules.",
"authors": [{
"fullName": "Roxana Bujack",
"givenName": "Roxana",
"surname": "Bujack",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Terece L. Turton",
"givenName": "Terece L.",
"surname": "Turton",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Francesca Samsel",
"givenName": "Francesca",
"surname": "Samsel",
"__typename": "ArticleAuthorType"
}, {
"fullName": "Colin Ware",
"givenName": "Colin",
"surname": "Ware",
"__typename": "ArticleAuthorType"
}, {
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vast2017titles.json
["A Utility-aware Visual Approach for Anonymizing Multi-attribute Tabular Data","ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models","Analyzing the Training Processes of Deep Generative Models","Applying Pragmatics Principles for Interaction with Visual Analytics","Beyond Tasks: An Activity Typology for Visual Analytics","BiDots: Visual Exploration of Weighted Biclusters","Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis","Clustering Trajectories by Relevant Parts for Air Traffic Analysis","Clustervision: Visual Supervision of Unsupervised Clustering","Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study","ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding","DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks","Do Convolutional Neural Networks learn Class Hierarchy?","Dynamic Influence Networks for Rule-based Models","EVA: Visual Analytics to Identify Fraudulent Events","EventThread: Visual Summarization and Stage Analysis of Event Sequence Data","Graphiti: Interactive Specification of Attribute-based Edges for Network Modeling and Visualization","How Do Ancestral Traits Shape Family Trees over Generations?","LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets","PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models","Podium: Ranking Data Using Mixed-Initiative Visual Analytics","Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework [Best Paper Honorable Mention Award]","Sequence Synopsis: Optimize Visual Summary of Temporal Event Data","SkyLens: Visual Analysis of Skyline on Multi-dimensional Data","SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance","Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs [Best Paper Honorable Mention Award]","The Interactive Visualization Gap in Initial Exploratory Analysis","Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics","TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees","Understanding a sequence of sequences: Visual exploration of categorical states in lake sediment cores","Understanding the Relationship between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy","VIGOR: Interactive Visual Exploration of Graph Query Results","Visual Diagnosis of Tree Boosting Methods","Visualizing Big Data Outliers through Distributed Aggregation","Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses","Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow [Best Paper Award]","Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data","A Visual Analytics System for Optimizing Communications in Massively Parallel Applications","A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations","CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization","CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data","E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media","Interactive Visual Alignment of Medieval Text Versions","Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces","QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations","The “y” of it Matters, Even for Storyline Visualization","The Anchoring Effect in Decision-Making with Visual Analytics","The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics","Understanding Hidden Memories of Recurrent Neural Networks","Visual Causality Analysis Made Practical","Visualizing Real-Time Strategy Games: The Example of StarCraft II","Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics"]
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