| MATCH (source:Place {id: "Amsterdam"}), (destination:Place {id: "London"}) | |
| CALL gds.shortestPath.dijkstra.stream({ | |
| nodeProjection: 'Place', | |
| relationshipProjection: { | |
| ROAD: { | |
| type: 'EROAD', | |
| properties: 'distance', | |
| orientation: 'UNDIRECTED' | |
| } | |
| }, |
| [Unit] | |
| Requires=zookeeper.service | |
| After=zookeeper.service | |
| [Service] | |
| Type=simple | |
| User=gb760 | |
| ExecStart=/bin/sh -c '/home/gb760/kafka/bin/kafka-server-start.sh /home/gb760/kafka/config/server.properties > /home/gb760/kafka/kafka.log 2>&1' | |
| ExecStop=/home/gb760/kafka/bin/kafka-server-stop.sh | |
| Restart=on-abnormal |
Contact:
| #!/usr/bin/env python | |
| # Copyright 2016 Emaad Ahmed Manzoor | |
| # License: Apache License, Version 2.0 | |
| # http://www.eyeshalfclosed.com/blog/2016/07/22/spark-streaming-statistics/ | |
| """ | |
| Get Spark Streaming microbatch statistics: | |
| - Batch start time | |
| - Scheduling delay (in seconds) for each microbatch |
www3.cs.stonybrook.edu/~emanzoor/streamspot/
Below are the bootstrap clusters used for the experiments in the StreamSpot paper for each of following datasets:
all (01-C50_k10_all.txt): Chunk length of 50, 10 clusters.ydc (02-C25_k5_ydc.txt): Chunk length of 25, 5 clusters.gfc (03-C50_k5_gfc.txt): Chunk length of 50, 5 clusters.
See the project website for more details.
Please report any issues to [email protected].
Running this requires having the following files in the same directory as calculate_cluster_statistics.py:
all_links.pall_tweets.p
See the project website for more details.
Please report any issues to [email protected].
The attention potential (as estimated in section 4), when evaluated on this Twitter dataset:
| import random | |
| import operator | |
| t = int(raw_input()) | |
| randint = random.randint | |
| def deterministic(a,b,c,n): | |
| no = 0 | |
| for p in xrange(n): | |
| for q in xrange(n): |