Graph database: All content tagged as Graph database in NoSQL databases and polyglot persistence
Two new releases in the graph databases space:
DEX Graph Database 4.5
The new DEX Graph Database release comes with pre-packaged graph algorithms—breadth and depth first traversal, shortest path, Gabow connectivity—available for Java, .NET, and C++. You can get the new version from here.
Neo4j 1.7 Milestone 1
As per Neo4j 1.7 milestone 1 update, this version features:
- improved Cypher
- SSL support
- improved Neo4j documentation
- high availability improvements (nb: there are recommended maintenance releases for Neo4j 1.5 and 1.6)
- upgraded Blueprints and Gremlin support
You can get Neo4j 1.7 from here.
Original title and link: Graph Databases Updates: DEX Graph Database 4.5 and Neo4j 1.7 Milestone 1 ( ©myNoSQL)
Even if my first post about the Micosoft research graph database Trinity is back from March last year, I haven’t heard much about it since. Based on my tip, Klint Finley published an interesting speculation about Trinity, Dryad, Probase, and Bing. Since then though, Microsoft moved away from using Dryad to Hadoop and I’m still not sure about the status of the Trinity project. But I have found a paper about the Trinity graph engine authored by Bin Shao, Haixun Wang, Yatao Li. You can read it or download it after the break.
We introduce Trinity, a memory-based distributed database and computation platform that supports online query processing and offline analytics on graphs. Trinity leverages graph access patterns in online and offline computation to optimize the use of main memory and communication in order to deliver the best performance. With Trinity, we can perform efficient graph analytics on web-scale, billion-node graphs using dozens of commodity machines, while existing platforms such as MapReduce and Pregel require hundreds of machines. In this paper, we analyze several typical and important graph applications, including search in a so- cial network, calculating Pagerank on a web graph, and sub-graph matching on web-scale graphs without using index, to demonstrate the strength of Trinity.
- Max de Marzi is lately my favorite source for graph data visualization posts
- Even if the diagram looks amazing I’m wondering if it would scale for larger data sets
- Even if I gave it some thought, I’m still not sure how graph databases can record historical relationship/the evolution of relationships in a graph. If you have any ideas I’d love to hear.
Original title and link: Neo4j and D3.js: Visualizing Connections Over Time ( ©myNoSQL)
An article in a German publication mentions (according to Google translator) that sones GraphDB is up for sale:
The administrator of sones GmbH Hartig, Dr. Oliver lawyer, said that the graph database of insolvent sones GmbH will be sold.
Original title and link: Insolvent Sones GraphDB Available for Sale ( ©myNoSQL)
A new version of InfiniteGraph, the graph database from Objectivity, was announced today. This release features:
- a plugin framework: Two kinds of plugins are supported. A navigator plugin bundles components that assist in navigation queries, such as result qualifiers, path qualifiers, and guides. The Formatter plugin formats and outputs results of graph queries.
- enhanced IG Visualizer: The advanced Visualizer is now tightly integrated with InfiniteGraph’s Plugin Framework allowing indexing queries for edges, the Formatter plugin framework export GraphML and JSON (built-in) or other user defined plugin formats.
- support for Tinkerpop Blueprints and Gremlin: InfiniteGraph provides a clean integration with Blueprints that is well suited for applications that want to traverse and query graph databases using Gremlin
A bit more details can be found in the InfiniteGraph 2.1 release notes.
Original title and link: InfiniteGraph 2.1 Features Gremlin Support and a Plugin Framework ( ©myNoSQL)
Found this list of use cases for graph databases in a follow up of a Neo4j webinar:
- Social networks
- Collaboration programs
- Configuration Management
- Geo-Spatial applications
- Impact Analysis
- Master Data Management
- Network Management
- Product Line Management
- Recommendation Engines
The more generic answer would be that graph databases can be a great fit for problems handling highly connected data.
The examples above are clear cases of use cases involving highly connected data , but as of now I’m not aware of any social networks, network management, or large scale recommendation engines built on top of one of the existing graph databases.
Original title and link: What types of applications might a graph database be well suited for? ( ©myNoSQL)