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A Graph Database

A Graph Database
Related:  Graph Database

high-performance graph database, data deduplication and bibliographic exploration Hypergraph An example of a hypergraph, with and is a pair where is a set of elements called nodes or vertices, and is a set of non-empty subsets of called hyperedges or edges. is a subset of , where is the power set of While graph edges are pairs of nodes, hyperedges are arbitrary sets of nodes, and can therefore contain an arbitrary number of nodes. A hypergraph is also called a set system or a family of sets drawn from the universal set X. There are variant definitions; sometimes edges must not be empty, and sometimes multiple edges, with the same set of nodes, are allowed. Hypergraphs can be viewed as incidence structures. Hypergraphs have many other names. Terminology[edit] Because hypergraph links can have any cardinality, there are several notions of the concept of a subgraph, called subhypergraphs, partial hypergraphs and section hypergraphs. Let be the hypergraph consisting of vertices and having edge set are the index sets of the vertices and edges respectively. induced by a subset of is defined as . has .

10 minutes pour comprendre...NoSQL - Blog de David MASCLET Le partitionnement Le point de contention des applications est bien souvent la base de données qui lorsqu'elles sont transactionnelles, distribuées, ne permettent pas facilement le passage à l'échelle. Que l'on fasse du lock optimiste, pessimiste ou transaction à deux phases, on couple toujours les données entre elles, ce qui empêche la sérialisation. En plus, pour la sécurité de fonctionnement, il faut que les données soient répliquées, se qui complique encore un peu plus les choses. Dans les architectures modernes, la validation du modèle se fait désormais dans les objets, au niveau de l'applicatif. Cela permet de soulager la base et de ne se concentrer que sur les requêtes. des mouvements comme le DDD ont remis au gout du jour les objets avec du code métier plutôt que des POJOs. Lorsque l'on veux scaler une base de données,une première solution peut être le partitionnement : Le théorème de CAP En voici les 3 principes : les sites ont fait leur choix ! Les 4 types de bases NoSQL conclusion

mapgraph: MapGraph HypergraphDB - A Graph Database What is TuProlog TuProlog a pure Java Prolog interpreter developed developed at the University of Bologna, Italy, see its home page for more information. The architecture of the interpreter is modular which makes relatively easy to extend. It also has a nice interface to Java and small memory footprint. TuProlog + HyperGraphDB The integration of TuProlog and HyperGraphDB has the following goals: Ability to store Prolog facts and rules (i.e. The idea is to work with HyperGraphDB data in a natural way, as if it was part of the Prolog system. Full API Javadocs (of the original tuProlog and our extensions) can be found here. Codebase Fork To achieve the stated goals, we had to fork the TuProlog codebase. Implementation Clause Stores The implementation relies on a newly added ClauseStoreManager that maintains a list of ClauseFactorys. This strategy allows arbitrary HGDB conditions to be treated as Prolog predicates and thus one can have a Prolog program backtrack through a HGDB result set. Usage

Orient Technologies - Open source solutions built around the Orient DB Graph Databases in Document Management | Graph database uses graph structures with nodes, edges, and properties to represent and store data.Compared with relational databases, graph databases are often faster for associative data sets, and map more directly to the structure of object-oriented applications. They can scale more naturally to large data sets as they do not typically require expensive join operations. As they depend less on a rigid schema, they are more suitable to manage ad-hoc and changing data with evolving schema. Conversely, relational databases are typically faster at performing the same operation on large numbers of data elements. Graph databases are a powerful tool for graph-like queries, for example computing the shortest path between two nodes in the graph. When should you use a graph database?

Les Bases Orientées Graphes, NoSQL et Neo4j Introduction Parmi les différents modèles de données, le modèle relationnel a dominé depuis les années 80, avec des implémentations telles qu'Oracle, MySQL et MSSQL - aussi connus sous le nom de Systèmes de Gestion de Bases de Données Relationnelles (SGBDR). Pourtant, ces derniers temps dans un nombre croissant de cas d'utilisations l'usage de bases de données relationnelles a rencontré des écueils à la fois à cause de problèmes et de manques dans la modélisation des données et à cause de contraintes de montée en charge horizontale, distribuée sur plusieurs serveurs et de gros volumes de données. Les bases de données relationnelles ont de plus en plus de mal à s'accommoder de ces tendances. Cet article a pour but de donner une vue d'ensemble de comment les bases de données orientées graphes se positionnent dans le mouvement NOSQL. L'environnement NOSQL En bref, les bases de données NOSQL peuvent être catégorisées selon leur modèle de données dans les 4 catégories suivantes: 1. 2.

Graph database Description[edit] Graph databases employ nodes, properties, and edges. Graph databases are based on graph theory, and employ nodes, edges, and properties. Nodes represent entities such as people, businesses, accounts, or any other item to be tracked. In contrast, graph databases directly store the relationships between records. The true value of the graph approach becomes evident when one performs searches that are more than one level deep. Properties add another layer of abstraction to this structure that also improves many common queries. Relational databases are very well suited to flat data layouts, where relationships between data is one or two levels deep. Properties[edit] Graph databases are a powerful tool for graph-like queries. History[edit] In the pre-history of graph databases, in the mid-1960s Navigational databases such as IBM's IMS supported tree-like structures in its hierarchical model, but the strict tree structure could be circumvented with virtual records.[5][6]

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