gremlin

And some Gremlin screencasts and presentations are here: https://github.com/tinkerpop/gremlin/wiki

Titan is distributed and has much higher write performance (5000 transactions/sec). In the words of Matthias Broecheler, the creator of Titan, you would use Titan to implement a social network (https://twitter.com/MBroecheler/statuses/213350753031569409). Titan has pluggable storage options: Cassandra, HBase, Berkeley DB, and some have been working on adapters for DynamoDB and App Engine Datastore. However, it's not clear how well App Engine and DynamoDB will work with the upcoming Titan changes which will move the traversal engine into the cluster's JVM. Both Neo4j and Titan support Blueprints, and you can run Gremlin (https://github.com/tinkerpop/gremlin/wiki) on both. Marko Rodriguez (http://markorodriguez.com/), the creator of Gremlin, is one of the principals behind Titan. See "The Benefits of Titan": https://github.com/thinkaurelius

Davy Suvee created a Dataomic Blueprints implementation (http://datablend.be/?p=1641) so you can run Gremlin (https://github.com/tinkerpop/gremlin/wiki) queries on it.

Davy implemented Datomic as a TinkerPop Blueprints graph (https://github.com/tinkerpop/blueprints/wiki/) so you can query it with Gremlin (https://github.com/tinkerpop/gremlin/wiki) too... "The use of Datoms and Datalog allows you to express simple, yet powerful queries. This article introduces only a fraction of the features offered by Datomic. To get myself better acquainted with the various Datomic gotchas, I implemented the Tinkerpop Blueprints API on top of Datomic. By doing so, you basically get a distributed, temporal graph database, which is, as far as I know, unique within the Graph database ecosystem. The source code of this Blueprints implementation can currently be found on the Datablend public GitHub repository and will soon be merged within the Tinkerpop project."

These are the base elements: * Nodes/Vertices: Person, Topic, Entry * Relationships/Edges: Tagged, Authored person -- authored --> entry entry -- tagged --> topic Here's the base model: https://github.com/espeed/lightbulb/blob/master/lightbulb/mo... The comment system is a separate, generic component. Marko, the guy who created Gremlin (https://github.com/tinkerpop/gremlin/wiki), just released a Gremlin Tree step (https://github.com/tinkerpop/gremlin/wiki/Tree-Pattern), which makes it really easy to build a threaded comment tree in one quick shot. If you have a large blog or content system, you can use likes and views to make content recommendations using a basic Gremlin collaborative filtering query: // calculate basic collaborative filtering for vertex with user_id def rank_items(user_id) { m = [:] g.v(user_id

The Neo4j User Group wants to help with this (https://groups.google.com/d/topic/neo4j/rkhjlQx-bfo/discussi...). Gremlin (https://github.com/tinkerpop/gremlin/wiki) works great for real-time recommendations. See "A Graph-Based Movie Recommender Engine" by Gremlin's creator, Marko Rodriguez (http://markorodriguez.com/2011/09/22/a-graph-based-movie-rec...)

Try loading the data into Neo4j and running Gremlin on it (https://github.com/tinkerpop/gremlin/wiki). Examples... Ranked similarity search https://groups.google.com/d/msg/neo4j/tuoqDlq45ys/1g1G9nTzjh... Multi-Item Based Recommendation https://groups.google.com/d/msg/neo4j/XlGGdsSedVw/7pCPqghA84... Recommendation engine https://groups.google.com/d/msg/neo4j/_dnyanUSmtk/eDhJ6rjNCn...

What advantages does this hold over doing the same thing with Gremlin? Other than being slightly prettier syntactically. [0] [0] https://github.com/tinkerpop/gremlin/wiki