Native hypergraph storage and processing for the utmost in schema flexibility.
The kind of real-time performance that only modern optimized C++ and lock/wait-free data structures can deliver.
Tunable on disk durability built on top of the excellent RocksDB storage system.
A pattern matching system that supports both easy querying and complex hypergraph rewriting.
Built for both read and write horizontal scalability, across cores and nodes, from the very start.
Per node Paxos-based cluster membership provides "breathe easy" failure recovery.
PatternSpace™ is the most fundamentally advanced graph database available today. It's purpose built for Big Data and, with its universal (hyper)graph core, is capable of naturally modeling the most complex of data sets. PatternSpace™'s performance, flexibility, and ease of use make it the best choice for next-generation applications and their analytics.
Correlating massive amounts of socio-economic events with time-based financial market data is easily done using the flexible graph storage capabilities of PatternSpace™. Highlighting patterns and discovering trends within that data can then be accomplished in PatternSpace™ by using its powerful pattern matching-based query engine.
Connecting patients with their records, healthcare personnel, and treatment options is a powerful use of PatternSpace™'s relational capabilities. Inferring logical similarities and reoccurring patterns throughout the history of a patient's healthcare yields enormous potential for innovative improvements in service and a new class of quality, personalized treatment possibilities.
PatternSpace™ can accommodate a variety of cyber security storage needs, from Identity Access Management's natural graph representation to Threat Modeling across networks. Use PatternSpace™ to ingest data from numerous endpoints, integrate them together, then use it to detect anomalous activity through its powerful pattern matching capabilities.
Finally, a converged data storage solution for the universal organization of all sources of defense data. Allow your defense personnel to access relevant, time-critical information across all intelligence sources for the utmost in situational awareness. PatternSpace™ provides the facilities required to store and process this data for real-time discovery of previously unrecognized insights in actionable time scales.
The intelligence community is highly dependent on high volumes of quality structured and unstructured information, curated and analyzed by intelligence personnel for the early detection of critical events and emergent threats. PatternSpace™ can ingest massive streams of increasingly interconnected or disparate intelligence data, all while allowing for the secure updating, auditing, and discovery of relevant insights by both analysts and their automated systems.
One of the most often cited examples of applied graph theory, social relationships perfectly map to the (hyper)graph data structure at the core of PatternSpace™. Take advantage of that natural (hyper)graph-based representation and, using PatternSpace™, perform real-time queries across billions of relationships to help make sense of the inherent depth of complexity between individuals.
(Hyper)graphs are well suited to many machine learning tasks, largely as a consequence of their data modeling flexibility. PatternSpace™'s performance focus helps make these compute intensive machine learning algorithms practical for daily use across massive data sets. Applicability covers Probabilistic Graphical Models (PGMs), Natural Language Processing (NLP), General Knowledge Representation, and much, much more.
Go beyond the basic analysis of data by identifying predictive indicators discoverable through PatternSpace™'s powerful pattern recognition. Taking the insights that exist within your data and highlighting their future impact can propel your organization's traditional passive analytics into the realm of powerful suggestive and solution-providing services.
Built into PatternSpace™ is a powerful temporal, or time-based, property type that can be assigned to any entity or relationship within the graph. Coupled with PatternSpace™'s data modeling flexibility, entities or entire related groups of entities can be further related and filtered by their occurrence within one or more time periods or at one or more points in time.
Model your enterprise's network connected infrastructure using PatternSpace™'s (hyper)graph database engine. Store servers, network devices, micro-services, and application availability in PatternSpace™ for centralized discovery and monitoring of your organization's infrastructure.
The power and applicability of PatternSpace™ is immense, as with (hyper)graphs in general. In addition to the above solutions, there exist many instances where unique circumstances or novel innovation makes for new and interesting use cases. We're happy to collaborate on projects such as these, helping to adapt PatternSpace™ to your problem set using the extensive experience and skills of our high caliber engineering team.