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AllegroGraph RDFStore Web 3.0's 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. Related:  Graph Databases

Objectivity – InfiniteGraph InfiniteGraph enables organizations to achieve greater return on their data related investment by helping them “connect the dots” on a global scale, ask deeper and more complex questions, across new or existing data stores. There is no other graph technology available today, offered by any other commercial vendor or open source project, that can match InfiniteGraph’s combined strengths of persisting and traversing complex relationships requiring multiple hops, across vast and distributed data stores. Download and develop, FREE with our 60 day trial period! Meet openCypher: The SQL for Graphs By Emil Eifrem, CEO | October 21, 2015 If you haven’t just heard the news from my keynote presentation at GraphConnect San Francisco, you missed what I believe is going to be a defining moment not only for Neo4j but the entire world of graph technologies. Setting aside a few small but smart players such as my buddy Marko Rodriguez and his merry band of graphistas at Aurelius, Neo4j has had the privilege and peril of being nearly alone in the graph database sector. With this mainstream momentum, it’s going to be beneficial for everyone if we can all agree on one common language to speak. The analogy to SQL couldn’t be clearer. The original relational database (RDBMS) players all started with their own individual query languages. Why We Need a Common Graph Query Language As more users learn about graphs and as more tools and vendors enter the graph space, we’re at a time when a shared graph query language – agnostic of vendor or platform – will be a huge benefit to both vendors and users.

Virtuoso Universal Server Database structure[edit] Core database engine[edit] Virtuoso provides an extended object-relational model, which combines the flexibility of relational access with inheritance, run time data typing, late binding, and identity-based access. Architecture[edit] Virtuoso is designed to take advantage of operating system threading support and multiple CPUs. The database has at all times a clean checkpoint state and a delta of committed or uncommitted changes to this checkpointed state. A transaction log file records all transactions since the last checkpoint. A single set of files is used for storing all tables. Locking[edit] Virtuoso provides dynamic locking, starting with row level locks and escalating to page level locks when a cursor holds a large percentage of a page's rows or when it has a history of locking entire pages. Transactions[edit] All four levels of isolation are supported: Dirty read, read committed, repeatable read and serializable. Data integrity[edit] Data dictionary[edit]

Enterprise Graph Built for Cloud Applications Graph databases can help make sense of highly connected data, but a graph database by itself isn’t enough to satisfy the needs of modern cloud applications. The power of a graph database can only be fully realized when paired with the functionality of advanced analytics, real-time indexing and search. With competitive offerings, each of these requirements would need to be satisfied by an individual point solution. Multi-Model Capabilities DataStax Enterprise Graph is part of the DataStax Enterprise multi-model platform, which provides support for key-value, tabular, JSON/Document, and graph data models. Enterprise Ready DataStax Enterprise Graph incorporates all of the enterprise-class functionality found in DataStax Enterprise. Commitment to Open Standards Built with Apache TinkerPop Apache TinkerPop™ is the industry standard open source graph computing framework that offers graph computing capabilities to database systems. Integrated with Apache Cassandra

Gremlin (programming language) Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors. The table below outlines those graph vendors that support Gremlin. user--rated[stars:0-5]-->movieuser--occupation-->occupationmovie--category-->category gremlin> g.V().label().groupCount()==>[occupation:21, movie:3883, category:18, user:6040] gremlin> g.V().hasLabel('movie').values('year').min()==>1919 gremlin> g.V().has('movie','name','Die Hard').inE('rated').values('stars').mean()==>4.121848739495798 Gremlin supports declarative graph pattern matching similar to SPARQL. The following traversal is a Gremlin traversal in the Gremlin-Java8 dialect. g.V().as("a").out("knows").as("b"). select("a","b"). by("name"). by("age") The Gremlin language (i.e. the fluent-style of expressing a graph traversal) can be represented in any host language that supports function composition and function nesting.

Graph rewriting Graph transformations can be used as a computation abstraction. The basic idea is that the state of a computation can be represented as a graph, further steps in that computation can then be represented as transformation rules on that graph. Such rules consist of an original graph, which is to be matched to a subgraph in the complete state, and a replacing graph, which will replace the matched subgraph. Formally, a graph rewriting system usually consists of a set of graph rewrite rules of the form , with being called pattern graph (or left-hand side) and being called replacement graph (or right-hand side of the rule). Sometimes graph grammar is used as a synonym for graph rewriting system, especially in the context of formal languages; the different wording is used to emphasize the goal of constructions, like the enumeration of all graphs from some starting graph, i.e. the generation of a graph language – instead of simply transforming a given state (host graph) into a new state. (or ) where

JanusGraph: Distributed graph database

allegrograph - high performance commercial rdf datastore, free upto 50 million tuples by balamurugan Dec 8