Nice data experiment run by Sebastien Goasguen against the CloudStack mailing list:
To get the graphs I grabbed the emails archive from Apache. I used
Python to load the mbox files into single Mongo collections. I
cleaned the data to avoid replications of senders as well as remove
JIRA and Review Board entries. Then with a little bit of PyMongo I
made the queries and build the graph with NetworkX. Finished up with
the graph visualization and calculations using Gephi. Since there
are thousands of emails and threads, there is still some work to
pre-process the data, avoid duplicates and match individuals to
multiple email addresses.
- would using a graph database made this experiment easier?
- would Linkurious be able to generate these graphics?
- is the code available anywhere so someone else could try to use a graph database and maybe run other types of visualizations?
Original title and link: Social Network Analysis of Apache CloudStack ( ©myNoSQL)
As I often run the same course, it would be interesting to calculate my average pace at specific locations. When combining the data of all of my courses, I could deduct frequently encountered locations. Finally, could there be a correlation between my average pace and my distance from home? In order to come up with answers to these questions, I will import my running data into a Neo4J Spatial datastore. Neo4J Spatial extends the Neo4J Graph Database with the necessary tools and utilities to store and query spatial data in your graph models. For visualizing my running data, I will make use of Gephi, an open-source visualization and manipulation tool that allows users to interactively browse and explore graphs.
This looks like a great application of a graph database for analyzing geo data. And it’s very practical.
Original title and link: Neo4J Spatial and Gephi for Smart Data Analysis ( ©myNoSQL)
You probably know by now that I love visualization tools:
Get the version of Gephi app that can read neo4j databases bzr branch http://bazaar.launchpad.net/~bujacik/gephi/support-for-neo4j: