Michael Hausenblas put together a list of hosted NoSQL solutions including Amazon DynamoDB and SimpleDB, Google App Engine, Riak, Cassandra, CouchDB, MongoDB, Neo4j, and OrientDB. If you go through my posts on NoSQL hosting , you’ll find a couple more.

Original title and link: NoSQL Hosting Services (NoSQL database©myNoSQL)

via: http://webofdata.wordpress.com/2012/03/18/hosted-nosql/

Being it IT, devops, or no-ops, operations are a critical part of every fairly sized system with real, expressed or not, SLAs. John E. Vincent’s post is an interesting look at what he feels is missing to make system operations more managable:

What I feel like we’re missing is a way to express those relationships and then trigger on them all the way up and down the chain as needed. We’re starting to get into graph territory here.

We must we be able to express and act on changes at the micro level (I changed a config, I must restart nginx) and even at the intranode level (something changed in my app tier, need to tell my load balancer) but now we need a way handle it at that macro level. Not only do we need a way to handle it but we must also be able to calculate what is impacted by that change.

Original title and link: How Can Graphs Apply to IT Operations (NoSQL database©myNoSQL)

via: http://blog.lusis.org/blog/2012/03/06/graphs-in-operations/

A directed network is simply a connection of nodes (representing teams) and arrows connecting teams called directed edges.  Every time a team defeated another, an arrow was drawn from the losing team’s node to the winning team’s node to represent this game.

Basketball and beers.

Original title and link: Using Graph Theory to Predict Basketball Teams Rankings (NoSQL database©myNoSQL)

via: http://blog.biophysengr.net/2012/03/eigenbracket-2012-using-graph-theory-to.html

Josh Adell explains how to extend a simple recommendation engine to similarity-based collaborative filtering:

Instead of basing recommendations off of one similar rating, I can calculate how similarly you and I rated all the things we have rated, and only get recommendations from you if I have determined we are similar enough in our tastes.

This is much closer to how recommendation engines developed by sites like Amazon or Netflix are working.

Original title and link: Beer Recommendations With Graph Databases (NoSQL database©myNoSQL)

via: http://blog.everymansoftware.com/2012/02/similarity-based-recommendation-engines.html

Romiko Derbynew comparing Gremlin and Neo4j Cypher:

• Simple graph traversals are much more efficient when using Gremlin
• Queries in Gremlin are 30-50% faster for simple traversals
• Cypher is ideal for complex traversals where back tracking is required
• Cypher is our choice of query language for reporting
• Gremlin is our choice of query language for simple traversals where projections are not required
• Cypher has intrinsic table projection model, where Gremlins table projection model relies on AS steps which can be cumbersome when backtracking e.g. Back(), As() and _CopySplit, where cypher is just comma separated matches
• Cypher is much better suited for outer joins than Gremlin, to achieve similar results in gremlin requires parallel querying with CopySplit, where as in Cypher using the Match clause with optional relationships
• Gremlin is ideal when you need to retrieve very simple data structures
• Table projection in gremlin can be very powerful, however outer joins can be very verbose

So in a nutshell, we like to use Cypher when we need tabular data back from Neo4j and is especially useful in outer joins.

Patrick Durusau

Original title and link: Gremlin vs Cypher (NoSQL database©myNoSQL)

via: http://romikoderbynew.com/2012/02/22/gremlin-vs-cypher-initial-thoughts-neo4j/

Marko Rodriguez is experimenting with R on Hadoop and one of his exercises is calculating a graph’s degree distribution. I confess I had to use Wikipedia for reminding what’s the definition of a node degree:

1. The degree of a node in a network (sometimes referred to incorrectly as the connectivity) is the number of connections or edges the node has to other nodes. The degree distribution P(k) of a network is then defined to be the fraction of nodes in the network with degree k.
2. The degree distribution is very important in studying both real networks, such as the Internet and social networks, and theoretical networks.

As an imagination exercise think of a graph database that’s actively maintaining an internal degree distribution and uses it to suggest or partition the graph. Would that work?

Original title and link: Calculating a Graph’s Degree Distribution Using R MapReduce over Hadoop (NoSQL database©myNoSQL)

via: http://groups.google.com/group/gremlin-users/browse_thread/thread/db50a72f92a26e06