Bigdata® Bigdata® Bigdata® is a horizontally scaled storage and computing fabric supporting optional transactions, very high concurrency, and very high aggregate IO rates. Bigdata® was designed from the ground up as a distributed database architecture running over clusters of 100s to 1000s of machines, but can also run in a high-performance single-server mode. Petabyte scale Dynamic sharding Commodity Hardware Open Source / Java Temporal database High Performance High Concurrency (MVCC) High Availability The bigdata® architecture provides a high-performance platform for data-intensive distributed computing, indexing, and high-level query on commodity clusters. Bigdata® RDF Database Bigdata® includes a high-performance RDF database supporting RDFS and limited OWL inference. The Bigdata® RDF Database was designed specifically for very large scale semantic alignment and federation of disparate data sets.
The Bigdata RDF Database provides the core features for a semantic web data tier, including: HyperGraphDB - A Graph Database. HyperGraphDB is a general purpose, extensible, portable, distributed, embeddable, open-source data storage mechanism. It is a graph database designed specifically for artificial intelligence and semantic web projects, it can also be used as an embedded object-oriented database for projects of all sizes.
The system is reliable and in production use is several projects, including a search engine and our own Seco scripting IDE where most of the runtime environment is automatically saved as a hypergraph. HyperGraphDB is primarily what its carefully chosen name implies: a database for storing hypergraphs. While it falls into the general family of graph databases, it is hard to categorize HyperGraphDB as yet another database because much of its design evolves around providing the means to manage structure-rich information with arbitrary layers of complexity. Key Facts Possible Usage Scenarios Semantic Web projects are an obvious domain of application of HyperGraphDB.
InfiniteGraph, the Distributed Graph Database. InfiniteGraph Overview 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.
Key Features Distributed Graph Database Take full advantage of distributed data using flexible and configurable storage locations, and distribute the processing load in the most efficient way for your application. You can add storage locations and zones to your graph database at any time, making them immediately available to any applications accessing the graph. Download and develop, FREE with our 60 day trial period!
Neo4j: NOSQL For the Enterprise. Introduction aux graphes avec Neo4j et Gephi. Les solutions permettant de modéliser, stocker et parcourir de façon efficiente des graphes ont profité de plusieurs éléments qui les ont rendues populaires ces dernières années. Le premier élément aidant à leur démocratisation est l’explosion des réseaux sociaux. Un cas d’usage évident, facile à comprendre même si, étrangement, les solutions mises en œuvre ne sont pas forcément de « type graphe » (par exemple avec FlockDB chez Twitter). Le second est lié au mouvement NoSQL qui a aidé à diffuser l’idée que la base relationnelle n’est pas la seule solution de stockage et de requêtage.
Enfin, et même si la théorie des graphes n’est pas neuve, les algorithmes sous-jacents et certaines implémentations ont atteint un niveau de maturité permettant la « commoditisation » de ces technologies, les aidant du même coup à sortir de zones très spécifiques. Alors qu’est-ce qu’un graphe? A quoi cela peut-il bien servir? En fait un graphe est une structure de donnée un peu généralisée. Property graphs.