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A Graph Database

A Graph Database
Related:  Graph Database

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 .

mapgraph: MapGraph nuvolabase/orientdb realtime analytics 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?

Graph database Graph databases, by design, allow simple and fast retrieval[citation needed] of complex hierarchical structures that are difficult to model[according to whom?] in relational systems. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction[2] and lack easy traversal over a chain of edges.[3] Description 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. Properties History List of graph databases See also References

cep app What is HyperGraphDB? Recently we’ve seen a lot of activity in the graph database world. Better understanding the space will help us make smarter decisions, so I’ve decided to reach out to the main players in the market and run a series of interviews about their projects and goals. The first in this series is about HyperGraphDB and Borislav Iordanov, his creator, has been kind enough to answer my questions. myNoSQL: What is HyperGraphDB? Borislav Iordanov: HyperGraphDB is a storage framework based on generalized hypergraphs as its underlying data model. myNoSQL: How would you position HyperGraphDB inside the NoSQL space? Boris: I think it is quite apart and I don’t see it fit into any particular category. myNoSQL: Would you mind explaining a bit more why you are placing HyperGraphDB closer to object databases than to graph databases? myNoSQL: What are other solutions in this category/space? Boris: I don’t know of any. Boris: Probably the two most interesting ones are: myNoSQL: Thanks a lot Boris!

Home - OrientDB Document-Graph NoSQL DatabaseOrientDB Document-Graph NoSQL Database Foreign exchange market The foreign exchange market (forex, FX, or currency market) is a global decentralized market for the trading of currencies. This includes all aspects of buying, selling and exchanging currencies at current or determined prices. In terms of volume of trading, it is by far the largest market in the world, followed by the Credit market.[1] The main participants in this market are the larger international banks. Financial centres around the world function as anchors of trading between a wide range of multiple types of buyers and sellers around the clock, with the exception of weekends. The foreign exchange market does not determine the relative values of different currencies, but sets the current market price of the value of one currency as demanded against another. The foreign exchange market works through financial institutions, and it operates on several levels. The foreign exchange market assists international trade and investments by enabling currency conversion. History[edit] Alex.

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