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Multi-Model Imagine a product with the flexibility of a Document Database and the ability to express relationships like a Graph Database. This is OrientDB. OrientDB supports HTTP and JSON out-of-the-box. You can start using OrientDB from curl or even your Web Browser. Read more about HTTP REST and JSON API.

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Titan: Distributed Graph Database Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. In addition, Titan provides the following features: Download Titan or clone from GitHub. Read the Titan documentation and join the mailing list. <dependency><groupId>com.thinkaurelius.titan</groupId><artifactId>titan-core</artifactId><version>1.0.0</version></dependency><! Graph database Structure[edit] Graph databases are based on graph theory. Graph databases employ nodes, properties, and edges. Graph database

AllegroGraph RDFStore Web 3.0's Database Geospatial and Temporal Reasoning AllegroGraph stores geospatial and temporal data types as native data structures. Combined with its indexing and range query mechanisms, AllegroGraph lets you perform geospatial and temporal reasoning efficiently. Social Networking Analysis AllegroGraph includes an SNA library that treats a triple-store as a graph of relations, with functions for measuring importance and centrality as well as several families of search functions. Example algorithms are nodal-degree, nodal-neighbors, ego-group, graph-density, actor-degree-centrality, group-degree-centrality, actor-closeness-centrality, group-closeness-centrality, actor betweenness-centrality, group-betweenness-centrality, page-rank-centrality, and cliques. Geospatial and temporal primitives combined with SNA functions form an Activity Recognition framework for flexibly analyzing networks and events in large volumes of structured and unstructured data.

OrientDB Manual 1.7.8 This is a comparison page between GraphDB projects. To know more about the comparison of DocumentDBs look at this comparison. We want to keep it always updated with the new products and more features in the matrix. If any information about any product is not updated or wrong, please change it if you've the permissions or send an email to any contributors with the link of the source of the right information. The products below all support the TinkerPop Blueprints API at different level of compliance. Below the supported ones. Berkeley DB Java Edition Oracle Berkeley DB Java Edition is an open source, embeddable, transactional storage engine written entirely in Java. It takes full advantage of the Java environment to simplify development and deployment. The architecture of Oracle Berkeley DB Java Edition supports very high performance and concurrency for both read-intensive and write-intensive workloads.

redis_graph 1.0 Package Index > redis_graph > 1.0 Not Logged In Status Nothing to report NoSQL GraphDB I received some constructive criticism regarding my previous blog in NoSQL patterns that I covered only the key/value store but have left out Graph DB. The Property Graph Model A property graph is a collection of Nodes and Directed Arcs. Each node represents an entity and has an unique id as well as a Node Type.

The Past, Present, and Future of Data Storage As we approach the end of 2011 and look forward to another year, we pause to reflect on the long history of data storage. Mankind's ability to create, process, store, and recall information is light years ahead of the days of cave paintings and engravings on stone tablets. Vast amounts of information can be stored on drives smaller than your thumb, and data centers are cropping up at an increasingly high rate. What does the future of data storage hold? Representing time dependent graphs in Neo4j · SocioPatterns/neo4j-dynagraph Wiki Background Large-scale data collection efforts using wearable sensors to mine for proximity of individuals (for example, the SocioPatterns project) produce time-varying social graphs, where nodes are individuals, edges represent proximity/contact relations of individuals, and the proximity graph changes over time. Both nodes and edges can have rich attributes. Data formats for exchanging the time-dependent graphs are available, see for instance the GEXF format.

MongoGraph Brings Semantic Web Features to MongoDB Developers MongoGraph from AllegroGraph team brings semantic web features to MongoDB developers. They implemented a MongoDB interface to AllegroGraph database to give Javascript programmers both joins and the semantic web capabilities. Using this approach JSON objects are automatically translated into triples and both the MongoDB query language and SPARQL work against these objects. Another goal of MongoGraph is to make the freetext engine of their graph database easy to search as Solr/Lucene.

Graph Databases, NOSQL and Neo4j Introduction Of the many different datamodels, the relational model has been dominating since the 80s, with implementations like Oracle, MySQL and MSSQL - also known as Relational Database Management System (RDBMS). Lately, however, in an increasing number of cases the use of relational databases leads to problems both because of Deficits and problems in the modeling of data and constraints of horizontal scalability over several servers and big amounts of data. There are two trends that bringing these problems to the attention of the international software community:

Blog - Using Datomic as a Graph Database Datomic is a database that changes the way that you think about databases. It also happens to be effective at modeling graph data and was a great fit for performing graph traversal in a recent project I built. I started out building using Neo4j, a popular open-source graph database. It worked very well for actors that were a few hops away, but finding paths between actors with more than 5 hops proved problematic. Polyglot Persistence and Query with Gremlin February 4, 2013 by Stephen Mallette Complex data storage architectures are not typically grounded to a single database. In these environments, data is highly disparate, which means that it exists in many forms, is aggregated and duplicated at different levels, and in the worst case, the meaning of the data is not clearly understood.