* The mathematical definition of a hypergraph is an extension to the standard graph concept that allows an edge to point to more than two nodes. * The basic unit of storage in HyperGraphDB is called an atom. Each atom is typed, has an arbitrary value and can point to zero or more other atoms. * Data types are managed by a general, extensible type system embedded itself as a hypergraph structure. Types are themselves atoms as everybody else, but with a particular role (well, as everybody else too). * The storage scheme is platform independent and can thus be accessed by any programming language from any platform. * Size limitations are virtually non-existent. There is no software limit on the size of the graph managed by a HyperGraphDB instance. * The current implementation is solely Java based. It offers an automatic mapping of idiomatic Java types to a HyperGraphDB data schema which makes HyperGraphDB into an object-oriented database suitable for regular business applications. * Embedded in-process: the database comes in the form of a software library to be used directly through its API. * A P2P framework for distributed processing has been implemented for replication/data partitioning algorithms as well as client-server style computing.

* Data Storage - Native storage structure with index-free adjacency results in faster transactions and processing. * Query Performance - Native processing ensures low latency and real-time performance, regardless of the number or depth of relationships. * Transaction Support - ACID transactions ensure data is fully consistent and reliable around the clock – perfect for always-on global enterprise applications. * Processing at Scale - Native graph model inherently scales for pattern-based queries. Scale out architecture maintains data integrity via replication. Massive scale up possibilities with IBM POWER8 and CAPI Flash systems.