Thanks Javier, and thanks Peter for pointing to our discussion page on github.
We are trying to assess the viability of Neo4j+Cypher for large-scale graph timelines such as the ones generated by our proximity-sensing platforms and we are facing some performance issues with Cypher on relatively small datasets, that we are trying to iron out.
The request for feedback is a way to open up that discussion and understan how the data model we describe should be improved, whether we are facing intrinsic performance limitations of Cypher/Neo4j for data with power-law distributed node connectivity, and what we can do about that all.
[looks like I managed to accidentally delete a previous post of mine to this thread]
Thanks Javier, and thanks Peter for stimulating some discussion on our wiki page.
We are evaluating the viability of Neo4j+Cypher to deal with graph timelines from wearable proximity sensors, and we would like to think more in general about representing and querying graph timelines (i.e., time-dependent graphs) in Neo4j.
In experimenting with the data model described in the wiki page, we faced rather unexpected (for us) performance issues with some Cypher queries, even on relatively small graphs such as the test data discussed in the wiki. We are trying to understand whether we should work more on the data model, on the queries, or whether we're facing intrinsic performance limitations of Neo4j+Cypher when dealing with highly-connected data.