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May 8, 2017 14:54
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| track | room | floor | start | end | title | speaker | abstract | tags | |
|---|---|---|---|---|---|---|---|---|---|
| Fika | General area | 3rd Floor | 830 | 915 | Registration and coffee | ||||
| Keynote | Fleming | 3rd Floor | 915 | 930 | Welcome and Introduction | Rik Van Bruggen | Just an Intro | intro, housekeeping | |
| Keynote | Fleming | 3rd Floor | 915 | 930 | Welcome and Introduction | Lars Nordwall | Just an Intro | intro, housekeeping | |
| Keynote | Fleming | 3rd Floor | 915 | 930 | Welcome and Introduction | Dirk Möller | Just an Intro | intro, housekeeping | |
| Keynote | Fleming | 3rd Floor | 930 | 1030 | Opening Keynote | Emil Eifrem | All the important stuff, brought to you by Emil. | intro, update, company, project, open source, future, strategy | |
| Business Impact Track | Fleming | 3rd Floor | 1100 | 1130 | Scaling Neo4j to Millions of Homes with Kubernetes | Rickard Damm | Scaling Neo4j to millions of homes with Kubernetes Telia Zone is a platform that allows consumers to experience superior digital solutions at home, simplifying everyday life by making time at home more enjoyable. Telia Zone gained a lot of press for launching both an open developer API and a consumer proposition in record time. Technical challenges, performance requirements and the strive to implement more advanced features has required the team to develop and implement the backend with the latest technologies and methods including Neo4j Casual Clustering and Kubernetes. | scale, IoT, kubernetes, containers | |
| Business Impact Track | Fleming | 3rd Floor | 1100 | 1130 | Scaling Neo4j to Millions of Homes with Kubernetes | Lars Ericsson | Scaling Neo4j to millions of homes with Kubernetes Telia Zone is a platform that allows consumers to experience superior digital solutions at home, simplifying everyday life by making time at home more enjoyable. Telia Zone gained a lot of press for launching both an open developer API and a consumer proposition in record time. Technical challenges, performance requirements and the strive to implement more advanced features has required the team to develop and implement the backend with the latest technologies and methods including Neo4j Casual Clustering and Kubernetes. | scale, IoT, kubernetes, containers | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1100 | 1130 | Building and Using a Neo4j 3.2 Causal Cluster | Alistair Jones | Building and Using a Neo4j 3.2 Causal Cluster How do you configure and run Neo4j on a cluster of servers, and how do you deploy it from an application? This presentation will explore thee Neo4j 3.2 causal clustering architecture and features, and how to build an application using those features via the Neo4j native language drivers. | 3.2, causal cluster, scale | |
| Solutions Track | Whittle | 3rd Floor | 1100 | 1130 | Neo4j as a Key Player in Human Capital Management (HCM) | Luanne Misquitta | Neo4j as a Key Player in Human Capital Management Graph databases are a perfect fit for HCM and people management solutions. In this talk, Luanne will present the challenges faced by these software vendors and how Neo4j can help them stay relevant and competitive. This talk demonstrates how Neo4j is the database that solves the core problem of the inability to process highly connected data in an efficient manner. The session will start with an introduction to typical components that comprise HCM software suites, such as Recruiting and Onboarding, Learning, Performance, and Talent. This is followed by examples in each of these areas and a discussion on how this data has been modelled traditionally (via RDBMS). Luanne will then share her experience using Neo4j to model people data in a more intuitive fashion, and how the examples presented earlier translate into graph traversals or Cypher queries. | recruitment, human capital management, rdbms, cypher | |
| Business Impact Track | Fleming | 3rd Floor | 1130 | 1200 | Don’t Choose One Database Choose Them All! | Dave da Silva | Don’t Choose One Database Choose Them All! Data scientists have a plethora of SQL and NoSQL databases to choose from, each offering a wide range of features that claim to make things easier, more efficient, cheaper and more productive. Looking past the marketing speak, some of these claims are true, which makes the question of which database to select even more difficult. On the Capgemini UK Data Science team we say,"Why settle on just one?”. This talk explains the rationale for using multiple databases based upon the business benefits that can be achieved for clients – despite the potential overhead of having to install, configure, populate and sync multiple data stores. | nosql, polyglot persistence, multimodel | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1130 | 1200 | Graphs at Scale | Johannes Unterstein | Graphs at scale - Scale out Neo4j using Apache Mesos and DC/OS In the era of Docker, Big Data and microservices, it's important to distribute your applications across your cluster and maintain a good overview of applications. A best practice is to locate your data in the same cluster similar to your application landscape and then share your physical resources across your running applications regardless of user requests, data crunching or large graph calculations. This is possible by incorporating Neo4j to run natively on top of Apache Mesos and DC/OS, and in this session we will demonstrate how to install, run, operate and scale out Neo4j Causal Clusters on top of Apache Mesos and DC/OS — and explore the opportunities this provides. | scale, mesos, containers, microservices | |
| Solutions Track | Whittle | 3rd Floor | 1130 | 1200 | Using Neo4j and Machine Learning to Create a Decision Engine | Tim Ward | Using Neo4j and Machine Learning to Create a Decision Engine There is no doubt that machine learning and graphs go hand in hand — and Neo4j makes it possible to take machine learning to the next level. In this presentation, we'll share how CluedIn built Neo4j into a decision engine that turns company data into actionable insights. By using machine learning techniques and the graph as a decision tree, we were able to achieve amazing precision in merging and identifying insights in the enterprise. With practical demos and techniques, viewers will leave this presentation with new and effective ways of working with Neo4j. | machine learning, decision engine | |
| Business Impact Track | Fleming | 3rd Floor | 1200 | 1230 | Neo4j and Bank Fraud | Syed Haniff | Creating a Data Distribution Knowledge Base using Neo4j | knowledge base, financial services, graph search | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1200 | 1230 | openCypher: Transactions and Analytics with Neo4j & Cypher for Spark | Alastair Green | openCypher: Multiple-Graph Analytics with Neo4j and Cypher for Apache Spark The openCypher project aims to turn the Cypher graph query language into an open, collaborative standard. The first openCypher Implementers Meeting (oCIM) at SAP's headquarters in February 2017 drew over 35 attendees from around ten companies and research groups. There are new features coming in Cypher, some of them already making it into the Neo4j product, others still in design. One big topic is "multiple graphs" – how to make Cypher a composable language that takes graphs in and pushes graphs out. This is a natural fit for graph analytic processing in the Apache Spark environment. Hear about transactions and analytics with Cypher, Neo4j and Spark, i.e., the future of graph data management. | cypher, opencypher, spark | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1200 | 1230 | openCypher: Transactions and Analytics with Neo4j & Cypher for Spark | Petra Selmer | openCypher: Multiple-Graph Analytics with Neo4j and Cypher for Apache Spark The openCypher project aims to turn the Cypher graph query language into an open, collaborative standard. The first openCypher Implementers Meeting (oCIM) at SAP's headquarters in February 2017 drew over 35 attendees from around ten companies and research groups. There are new features coming in Cypher, some of them already making it into the Neo4j product, others still in design. One big topic is "multiple graphs" – how to make Cypher a composable language that takes graphs in and pushes graphs out. This is a natural fit for graph analytic processing in the Apache Spark environment. Hear about transactions and analytics with Cypher, Neo4j and Spark, i.e., the future of graph data management. | cypher, opencypher, spark | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1200 | 1230 | openCypher: Transactions and Analytics with Neo4j & Cypher for Spark | Stefan Plantikow | openCypher: Multiple-Graph Analytics with Neo4j and Cypher for Apache Spark The openCypher project aims to turn the Cypher graph query language into an open, collaborative standard. The first openCypher Implementers Meeting (oCIM) at SAP's headquarters in February 2017 drew over 35 attendees from around ten companies and research groups. There are new features coming in Cypher, some of them already making it into the Neo4j product, others still in design. One big topic is "multiple graphs" – how to make Cypher a composable language that takes graphs in and pushes graphs out. This is a natural fit for graph analytic processing in the Apache Spark environment. Hear about transactions and analytics with Cypher, Neo4j and Spark, i.e., the future of graph data management. | cypher, opencypher, spark | |
| Solutions Track | Whittle | 3rd Floor | 1200 | 1230 | Fraud Detection and What We Can Learn from the Panama Papers | Dr. Jesús Barrasa | Fraud Detection and What We Can Learn from the Panama Papers A year ago, the ICIJ publicly released the Panama Papers. What lessons have we learned for modern fraud detection? Traditionally, fraud prevention focuses on discrete data points and detection of outliers. However, today’s sophisticated fraudsters escape detection by collaborating in groups and being locally indistinguishable from other users, which is why it is essential to look beyond individual data points to the connections that link them. In this talk, we will see how enterprise organisations use Neo4j to augment existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering. | fraud, panama papers, icij, journalism | |
| Fika | General area | 1st Floor | 1030 | 1100 | Coffee Break | ||||
| Fika | General area | 2nd Floor | 1030 | 1100 | Coffee Break | ||||
| Fika | General area | 3rd Floor | 1030 | 1100 | Coffee Break | ||||
| Fika | General area | 1st Floor | 1230 | 1400 | Lunch | ||||
| Fika | General area | 2nd Floor | 1230 | 1400 | Lunch | ||||
| Fika | General area | 3rd Floor | 1230 | 1400 | Lunch | ||||
| Business Impact Track | Fleming | 3rd Floor | 1400 | 1430 | Democratizing Data at Airbnb | Chris Williams | Democratizing Data at Airbnb As Airbnb grows, so do the challenges around the volume, complexity and obscurity of data, which is often siloed and lacks context. The Dataportal is an internal data tool, built on Neo4j, which empowers Airbnb employees to be data-informed by aiding with data exploration, discovery and trust. | scale, complexity, exploration, discovery, integration | |
| Business Impact Track | Fleming | 3rd Floor | 1400 | 1430 | Democratizing Data at Airbnb | John Bodley | Democratizing Data at Airbnb As Airbnb grows, so do the challenges around the volume, complexity and obscurity of data, which is often siloed and lacks context. The Dataportal is an internal data tool, built on Neo4j, which empowers Airbnb employees to be data-informed by aiding with data exploration, discovery and trust. | scale, complexity, exploration, discovery, integration | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1400 | 1430 | Securing your Neo4j Cluster with LDAP and Kerberos | Johan Teleman | Securing Neo4j Cluster with LDAP and Kerberos Kerberos is a widespread and highly secure protocol for authentication, which protects user credentials and reduces the need for network communication between your Neo4j cluster and the authenticating server. Kerberos can further provide an attractive end-user experience via the single-sign-on mechanism. In this session, we will explore how Kerberos can be used to secure a Neo4j cluster together with LDAP authorization, and what this means for system admins and app developers. | security, ldap, kerberos, cluster, active directory, administration | |
| Solutions Track | Whittle | 3rd Floor | 1400 | 1430 | moOn: A Multidimensional Graph Approach to Human Resources Analytics | Claudio Borile | moOn: A multidimensional graph approach to Human Resources Analytics The concept of the enterprise social graph (Forbes, 2010) has been garnering more and more attention over the last several years from increasingly large companies. The idea is simple: Exploit an enterprise's internal social media network to provide an agile management and analysis tool for HR. However, not all companies can rely on specific in-house social platforms to gather and/or analyze data, nor do they always have the competencies or time to handle such huge volumes of data. In this presentation, we will share innovative methods to visualize, navigate and manage a company's employee network to facilitate the management of key HR practices — which is rooted in Neo4j, Cypher and Python. We will demonstrate the effectiveness of this approach through the use of real company data. | human resources, cypher, python | |
| Business Impact Track | Fleming | 3rd Floor | 1430 | 1500 | Configuration Managemt at German National Rail | Marcel Donges | Configuration Management at Deutsche Bahn - a Graph-based CMM 4 Managed Environment for Data Centers In this talk, Marcel and Axel will illustrate how a team at the Deutsche Bahn subsidiary DB Systel built a CMM-4 (Capability Maturity Model) configuration and environment management tool using graph technology. DB Systel GmbH is the internal Information and Communication Technology (ICT) partner of Deutsche Bahn, both on national and international levels. To optimize the whole lifecycle of ICT solutions – from planning and development to operation and optimisation – the team at DB Systel created a central and highly automated solution for the management of configuration data for any application, storage and computing environment including software, hardware and network components within the DB AG group. | configuration management, cmm, rail | |
| Business Impact Track | Fleming | 3rd Floor | 1430 | 1500 | Configuration Managemt at German National Rail | Axel Morgner | Configuration Management at Deutsche Bahn - a Graph-based CMM 4 Managed Environment for Data Centers In this talk, Marcel and Axel will illustrate how a team at the Deutsche Bahn subsidiary DB Systel built a CMM-4 (Capability Maturity Model) configuration and environment management tool using graph technology. DB Systel GmbH is the internal Information and Communication Technology (ICT) partner of Deutsche Bahn, both on national and international levels. To optimize the whole lifecycle of ICT solutions – from planning and development to operation and optimisation – the team at DB Systel created a central and highly automated solution for the management of configuration data for any application, storage and computing environment including software, hardware and network components within the DB AG group. | configuration management, cmm, rail | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1430 | 1500 | Debunking some RDF-vs-PropertyGraph Alternative Facts | Dr. Jesús Barrasa | Debunking some RDF-vs-PropertyGraph Alternative Facts Is RDF for unstructured data while property graphs are for highly structured data? Will the RDF model discover new knowledge for me? Is RDF AI? Does RDF exclusively live in triple stores? All of these are statements have been published by analysts and vendors, building a wall of misconceptions between the two worlds that are not helpful for your new graph project. In this talk, we will dig deeper into the similarities and differences between the two main approaches to modelling graph data, focusing on debunking some of the ‘alternative facts’ built over the years. | RDF, property graph, semantic web, triple store | |
| Solutions Track | Whittle | 3rd Floor | 1430 | 1500 | Real-Time Recommender Systems Made Easy with Neo4j | Pieter Caillau | Real-time Recommender Systems Made Easy with Neo4j Recommender system technology is the core of Netflix and Amazon's business model and has lead to a tremendous increase in sales and customer satisfaction. Other retailers have seen sales increases of 5-15%, and now recommender systems are making their way to other industries to help customers find products faster, help salespeople find collateral and configure solutions, and help companies accelerate their product development by finding the right components to make products that meet market needs. Real-time recommender systems are one of the sweetspot use cases for native graph databases. Key goals for a good recommender system include relevance, novelty, serendipity and recommendation differentiation. In this talk, Pieter will demonstrate how you can have full and accurate control of the recommender system with Neo4j, interactive response at scale, and "on the fly" tuning for a fast time to market. | recommender system, recommendation, real-time | |
| Fika | General area | 1st Floor | 1500 | 1520 | Coffee Break | ||||
| Fika | General area | 2nd Floor | 1500 | 1520 | Coffee Break | ||||
| Fika | General area | 3rd Floor | 1500 | 1520 | Coffee Break | ||||
| Business Impact Track | Fleming | 3rd Floor | 1520 | 1550 | UK Rail Ticketing Assistance the Graph Way | Andy Smale | UK Rail Ticketing Assistance from the Graph Way "The UK Rail network is complex with more than twenty operators managing over six thousand locations running thousands of different services and millions of products sold. The underlying systems are all being updated and KCOM are delivering the replacement Availability and Reservations Service with an engine built on top of Neo4j. The challenge is to build a service capable of handling millions of requests per day with under 50 ms response times whilst maintaining a complex demand model to help the privatised rail companies maximise their revenue. | rail, reservations, scale, speed | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1520 | 1550 | A Practical Guide to Enterprise Metadata Handling in Data Driven Environments | Mirko Kämpf | A Practical Guide to Enterprise Metadata Handling in Data Driven Environments Neo4j has proven to be an enterprise-grade graph store and graph management system. Using the wiki-based knowledge graph as a blueprint for business knowledge management, we developed our Etosha toolbox to manage technical metadata with a business directory that links technical records and human knowledge. This leads to higher efficiency in our data exploration and data science procedures. In this presentation, we will demonstrate our collaboration tools by running them in two modes — locally on a single workstation and as a distributed environment across organizations — and will review the architecture of the underlying software. | MDM, metadata, cloudera, etosha, nosql, hadoop | |
| Solutions Track | Whittle | 3rd Floor | 1520 | 1550 | Graph-Powered Data Lineage in Finance | Jean Villedieu | Graph-Powered Data Lineage in Finance Tracking the flow of data is the foundation for solid data governance. It's also a compliance imperative for financial institutions impacted by BCBS 239. In this talk, we will discuss how graph-oriented data lineage is well suited for today's growing data volume and complexity. You will learn how to answer questions like: What would be the impact of a component of my data pipeline breaking up? Where does the data from a particular report originate? | MDM, data lineage, finance, banking, bcbs 239, regulatory compliance | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1550 | 1620 | Cypher Performance Improvements | Andrés Taylor | Cypher Performance Improvements For this release cycle, we have been busy improving the performance of Neo4j in many different ways. In this presentation, the creator of Cypher will go through what has been boosted in the 3.2 release of Neo4j. | cypher, query language, performance, speed, future | |
| Solutions Track | Whittle | 3rd Floor | 1550 | 1620 | Using Neo4j to Optimize Planning and Operations for California’s Largest Casino | Joe Stefaniak | Using Neo4j to Optimize Planning and Operations for California’s Largest Casino This session will center on a major US casino operator that analyzes data on one of the largest casino floors in the US by using a KeyLines graph visualization component with intelligentTag’s Symmetry3 compliance technology. With data stored in Neo4j, the casino’s analysts can visualize activity and discover graph insights that help them optimize and improve their operations. | visualization, optimization, casino, gambling | |
| Solutions Track | Whittle | 3rd Floor | 1550 | 1620 | Using Neo4j to Optimize Planning and Operations for California’s Largest Casino | Stuart Kerr | Using Neo4j to Optimize Planning and Operations for California’s Largest Casino This session will center on a major US casino operator that analyzes data on one of the largest casino floors in the US by using a KeyLines graph visualization component with intelligentTag’s Symmetry3 compliance technology. With data stored in Neo4j, the casino’s analysts can visualize activity and discover graph insights that help them optimize and improve their operations. | visualization, optimization, casino, gambling | |
| Fika | General area | 1st Floor | 1620 | 1640 | Coffee Break | ||||
| Fika | General area | 2nd Floor | 1620 | 1640 | Coffee Break | ||||
| Fika | General area | 3rd Floor | 1620 | 1640 | Coffee Break | ||||
| Business Impact Track | Fleming | 3rd Floor | 1640 | 1710 | Panel: Data Journalism in the Connected Age | Friedrich Lindenberg | Data Journalism in the Connected Age Panel Every day, data journalists have to deal with the growing volume and complexity of modern data leaks. In addition, uncovering (the often hidden) connections at both global and local scales is increasingly important. Graph databases help data journalists make sense of their data and connect it to many other sources, making it easier to calculate the meaning and impact of the dataset. In this high-profile panel, five data journalists from renowned publishers and organizations will discuss changes in the journalistic landscape, the relevance of data connections, and how modern tools such as Neo4j can support journalists in their daily challenges and work. | journalism, panama papers, visualization, panel | |
| Business Impact Track | Fleming | 3rd Floor | 1640 | 1710 | Panel: Data Journalism in the Connected Age | Meredith Broussard | Data Journalism in the Connected Age Panel Every day, data journalists have to deal with the growing volume and complexity of modern data leaks. In addition, uncovering (the often hidden) connections at both global and local scales is increasingly important. Graph databases help data journalists make sense of their data and connect it to many other sources, making it easier to calculate the meaning and impact of the dataset. In this high-profile panel, five data journalists from renowned publishers and organizations will discuss changes in the journalistic landscape, the relevance of data connections, and how modern tools such as Neo4j can support journalists in their daily challenges and work. | journalism, panama papers, visualization, panel | |
| Business Impact Track | Fleming | 3rd Floor | 1640 | 1710 | Panel: Data Journalism in the Connected Age | Helena Bengtsson | Data Journalism in the Connected Age Panel Every day, data journalists have to deal with the growing volume and complexity of modern data leaks. In addition, uncovering (the often hidden) connections at both global and local scales is increasingly important. Graph databases help data journalists make sense of their data and connect it to many other sources, making it easier to calculate the meaning and impact of the dataset. In this high-profile panel, five data journalists from renowned publishers and organizations will discuss changes in the journalistic landscape, the relevance of data connections, and how modern tools such as Neo4j can support journalists in their daily challenges and work. | journalism, panama papers, visualization, panel | |
| Business Impact Track | Fleming | 3rd Floor | 1640 | 1710 | Panel: Data Journalism in the Connected Age | Leila Haddou | Data Journalism in the Connected Age Panel Every day, data journalists have to deal with the growing volume and complexity of modern data leaks. In addition, uncovering (the often hidden) connections at both global and local scales is increasingly important. Graph databases help data journalists make sense of their data and connect it to many other sources, making it easier to calculate the meaning and impact of the dataset. In this high-profile panel, five data journalists from renowned publishers and organizations will discuss changes in the journalistic landscape, the relevance of data connections, and how modern tools such as Neo4j can support journalists in their daily challenges and work. | journalism, panama papers, visualization, panel | |
| Neo4j Deep-Dive | Churchill | Ground Floor | 1640 | 1710 | Interpreting Relational Schema to Graphs | Praveena Fernandes | Interpreting Relational Schema to Graphs With all of the Neo4j success stories, more and more people are eager to migrate from relational databases (RDBMS) to graph. But the number one question is — how do we do it? Over the last few months, I've developed an in-house tool to facilitate this process. Neo4j is a schema-optional database, which can be somewhat intimidating for people who are used to using schemas to structure data. But at its core, an entity-relationship diagram is a meta-graph, which allows us to apply a few simple rules to transform normalised relational schemas into a reasonable graph model. Attend this talk to learn how to use the Neo4j ETL tool to import a MySQL database into Neo4j by applying a few simple rules. We'll also cover lessons learned about schemas and common patterns along the way. | rdbms, migration, modeling | |
| Solutions Track | Whittle | 3rd Floor | 1640 | 1710 | Implementing a Real-Time Streaming Recommendation Engine within Two Weeks with Neo4j | David Stephenson | Implementing a Real-Time Streaming Recommendation Engine within Two Weeks Recommendation engines are critical in today’s ecommerce sector. They dramatically increase sales while simultaneously improving the customer shopping experience by helping customers quickly identify and locate the most relevant items. Implementing such a recommendation engine in production, however, can be a very expensive and time-consuming process. In this case study, Dr. Stephenson will explain how the Axel Springer digital group creatively leveraged existing technologies to build a real-time streaming implementation on an existing ecommerce platform — in only two weeks. Neo4j was used as a key part of the solution architecture that made this launch possible. | recommender system, recommendation, fast | |
| Fika | General area | 1st Floor | 1710 | 1740 | Coffee Break | ||||
| Fika | General area | 2nd Floor | 1710 | 1740 | Coffee Break | ||||
| Fika | General area | 3rd Floor | 1710 | 1740 | Coffee Break | ||||
| Keynote | Fleming | 3rd Floor | 1740 | 1830 | Closing Keynote | Jim Webber | science, comedy, future, research, standup | ||
| Fika | General area | 3rd Floor | 1830 | 2000 | GraphParty | ||||
| Lightning Track | Burton & Gielgud | 2nd Floor | 1100 | 1115 | Journey Planning and Why I Love Cypher | Adam Cowley | Lightning Talk: Journey Planning and Why I Love Cypher In this talk, Adam Cowley will give an overview of the challenges of building a prototype door-to-door journey planner with Neo4j. He'll show how to import multiple open data feeds into the graph and query across them with a couple hundred lines of Cypher – the graph query language. | cypher, pathfinding, planning | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1115 | 1130 | Power of Stored Procedures | Clark Richey | Lightning Talk: Extend the Power of Neo4j with Stored Procedures and APOC This talk will introduce crucial aspects of Neo4j stored procedures, which are a powerful and easy-to-implement feature made available in Neo4j 3.0. However, to take full advantage of this new capability, developers need to understand the fundamentals of stored procedures in Neo4j. This talk provides those fundamentals, complete with code examples and benchmarks in order to demonstrate the differences between the REST, embedded and BOLT APIs vs stored procedures; common database access patterns that benefit from stored procedures; the performance benefits of stored procedures; and the basics of creating a stored procedure. | java, apoc, stored procedures | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1130 | 1145 | Graphs in Time and Space: A Visual Example | Dan Williams | Lightning Talk: Graphs in time and space: A visual example Graph databases are helping to solve some of today’s most pressing challenges. From managing critical infrastructure and understanding cyber threats to detecting fraud, we have worked with hundreds of developers building all kinds of mission-critical graph applications powered by Neo4j. This presentation will explore two dimensions of graphs that, from our experience, cause the most confusion but potentially contain vital data insight: space and time. Dan will use visual examples to explain the quirks (and importance) of dynamic and geospatial graphs, and how they can be stored, explored and queried in Neo4j. He will then show how graph visualization tools empower users to explore connections between people, events, locations and times. | visualization, time, spatial | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1145 | 1200 | Decyphering your Graph Model | Dom Davis | Lightning Talk: Decyphering your graph model The property graph — which is a midway point between many nodes holding no data, and one node holding all the data — is just an abstraction that saves us from the insanity that could otherwise ensue. But is this the only abstraction we can use? Sometimes it makes sense to look at your data from higher levels, using the language of the domain and leaving the nitty-gritty to those who actually need to understand it. This session is a quick look at one such layout where it's actually graphs all the way down. | cypher, modeling | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1200 | 1215 | Understanding & Visualizing Complex Neo4j Instances | Sebastian Müller | Lightning Talk: Understanding and Visualizing Complex Neo4j Instances Neo4j makes it simple to collect, organize, query and analyze structured data. However, with larger datasets it can become difficult to understand or manage the structure as well as the contents of your database. Sebastian presents a tool that allows users to quickly inspect their running Neo4j database instance and both analyze the structure of the data and interactively explore the graph to easily gain new insights. | visualization, cluster | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1215 | 1230 | Fraud Detection Cookbook | Darko Križić | Lightning Talk: Fraud Detection Cookbook This talk is a recipe for how to use Neo4j to detect fraud based on simple examples. Today’s requirement for detecting fraud is much more complex than it was a few years ago - we talk about complex data. Neo4j is the perfect technology to create a semantic graph and Cypher is a very powerful query language to detect irregularities and frauds. | fraud, fraud detection | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1400 | 1415 | Graph Analysis over JSON | Omar Rampado | Lightning Talk: Graph analysis over JSON data Moving from a pure relational database to a polyglot persistence approach is the key to solving dedicated use cases effectively. Each database handles its core data model best, and their combination provides the best of both worlds for application development productivity and efficient large-scale data management. While working on many integration projects we've seen the need for well-engineered connectors, since combining different databases requires data synchronization and a communication solution, especially for document-oriented databases. In this talk, we'll introduce the doc2graph project, which is a solution that allows you to easily analyze relationships hidden in document data. We'll demonstrate examples for Couchbase, MongoDB and a generic API returning JSON data and how to configure the connector to get the best graph model for your analysis. | graph analysis, json, integration, doc2graph, mongodb, couchdb, polyglot persistence, nosql | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1415 | 1430 | Graph at the Core of a Microservices Architecture | Abed Halawi | Lightning Talk: Graph at the Core of a Microservices Architecture Managing data in a microservices architecture has never been a straightforward task. Splitting the monolith is already a complex process, let alone planning a product’s infrastructure from scratch with microservices in mind. A lot of effort has been put into finding solutions for data integration in a microservices architecture — all of which are successful when you put graphs at the heart of it. This talk will shed the light on our journey, covering how to set up Neo4j in a microservices architecture using AWS, how to consolidate data in the graph as the source of truth, and how to use graphs as a tool to model and describe the architecture, the fleet of services, their states (metadata), and their relationships as dependencies. | microservices, aws, amazon web services, cloud, dependencies | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1430 | 1445 | Panoptes: Our collective Intelligence to Get the Best Pitch for our Clients | Gautier Sartorius | Panoptes: Using Collective Intelligence to get the Best Pitch for our Clients Panoptes is a knowledge database of Crédit Agricole CIB (CACIB) clients, aggregating and cross checking publicly available information to offer a centralized and complete view of our clients’ news based on press reviews. Panoptes relies on Neo4j to provide the user with the most relevant data, and suggests content according to their navigation history. | knowledge management, investment banking, banking | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1445 | 1500 | ingraph: Live Queries on Graphs | Gabor Szarnyas | ingraph: Live Queries on Graphs What is the common challenge in detecting frauds in financial transactions, analyzing source code repositories and performing runtime verification on cyber-physical systems? These applications operate on large, continuously changing graphs and use complex queries, but also require quick response times. However, these queries are usually known in advance, so a smart query engine can rely on previous results and only calculate the differences of the last change. ingraph is a query engine for continuous and live queries. It allows users to register graph queries, precompute their results and evaluate changes in milliseconds. | cypher, query management, scale, complexity, speed, performance | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1520 | 1535 | Big Data Governance with Neo4j | Nicolas Rouyer | Big Data Governance with Neo4j In this talk, Nicolas will show how to pilot big data governance with Neo4j by covering how to: manage fine-grained rights on data, define complex authorization workflows, establish links between data sources, schedule data transfers and computes, apply certified algorithms over crossed data sources, and track and audit the whole data flow. | big data, governance, access control, authorization, auditing | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1535 | 1550 | Graphs in the (Digital) Humanities | Andreas Kuczera | Lightning Talk: Graphs in the (Digital) Humanities The humanities deal with large amounts of books with historical information, many of which were digitized in the last 15 years and are available online as searchable full text. Graph technologies are used to annotate these entities – persons, events and places as well as their connections and actions – turning this volume of information into a dense network of entities. These connections have provided astonishing new insights, and in this talk Andreas will share use cases and projects in the humanities that have benefited from graph technologies and highlight which new kinds of insights we can get from this powerful combination. | humanities, full-text search, insights | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1550 | 1605 | APOC: Extending the Power of Neo4j | Michael Hunger | Lightning Talk: APOC: Extending the Power of Neo4j | apoc, stored procedure, plugin | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1550 | 1605 | Semantic Content Management with Python & Neo4j | Andreas Schöller | Lightning Talk: Semantic Content Management with Python and Neo4j In this talk, we introduce a semantic content management server built on top of Neo4j and Python's Pyramid web framework. We focus on the metadata of content and integrate internal as well as external data sources. The system can be used as "a thinking person's thinking tool," e.g. for a semantic-enhanced blog. This talk will cover bootstrapping; standardized interchange; editing type (schema) information, binary data and large text files in several formats; the storage of queries; and strengthening Neo4j and graph technologies in the Python community. This talk will shed the light on our journey, covering how to set up Neo4j in a microservices architecture using AWS, how to consolidate data in the graph as the source of truth, and how to use graphs as a tool to model and describe the architecture, the fleet of services, their states (metadata), and their relationships as dependencies. | semantic web, python, metadata, integration, microservices, dependencies | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1550 | 1605 | Semantic Content Management with Python & Neo4j | Jörg Baach | Lightning Talk: Semantic Content Management with Python and Neo4j In this talk, we introduce a semantic content management server built on top of Neo4j and Python's Pyramid web framework. We focus on the metadata of content and integrate internal as well as external data sources. The system can be used as "a thinking person's thinking tool," e.g. for a semantic-enhanced blog. This talk will cover bootstrapping; standardized interchange; editing type (schema) information, binary data and large text files in several formats; the storage of queries; and strengthening Neo4j and graph technologies in the Python community. This talk will shed the light on our journey, covering how to set up Neo4j in a microservices architecture using AWS, how to consolidate data in the graph as the source of truth, and how to use graphs as a tool to model and describe the architecture, the fleet of services, their states (metadata), and their relationships as dependencies. | semantic web, python, metadata, integration, microservices, dependencies | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1605 | 1620 | INVESTIGRAPH: Using Neo4j for Investigative Journalism | Manuel Villa | INVESTIGRAPH: Using Neo4j for Investigative Journalism The Panama Papers opened our eyes to the idea of using graph data for journalism to find stories within big data. Since then, investigative journalists have found innovative ways to use graph data to map out investigations, and match it with publicly available information to find leads and invisible connections. This session will explore what works, what hasn’t worked and where we hope this technology will take us. | journalism, panama papers, investigation | |
| Lightning Track | Burton & Gielgud | 2nd Floor | 1605 | 1620 | INVESTIGRAPH: Using Neo4j for Investigative Journalism | Sarah Blaskey | INVESTIGRAPH: Using Neo4j for Investigative Journalism The Panama Papers opened our eyes to the idea of using graph data for journalism to find stories within big data. Since then, investigative journalists have found innovative ways to use graph data to map out investigations, and match it with publicly available information to find leads and invisible connections. This session will explore what works, what hasn’t worked and where we hope this technology will take us. | journalism, panama papers, investigation | |
| GraphClinics | Britten | 3rd Floor | 1100 | 1740 | Neo Technology Consultants, Engineers and Developer Evangelists | questions, answers, clinic |
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