Franz Inc., supplier of Graph Database technology for AI knowledge graph solutions, announced AllegroGraph 7.1, which provides optimizations for complex queries across FedShard deployments faster. AllegroGraph with FedShard allows infinite data integration through unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution for Enterprise scale analytics. Big Data predictive analytics requires a data model approach that unifies typical enterprise data with knowledge bases such as taxonomies, ontologies, industry terms and other domain knowledge. The Entity-Event Data Model utilized by AllegroGraph puts core ‘entities’ such as customers, patients, students or people of interest at the center and then collects several layers of knowledge related to the entity as ‘events’. The events represent activities that transpire in a temporal context.

The AllegroGraph 7.1 release accelerates complex reasoning across enterprise-scale data by providing users with additional query options. Franz’s Research and Development team discovered an approach that can significantly improve certain SPARQL Path Expression queries across database shards. AllegroGraph’s advanced caching methods and merge join operations provide optimizations to the scalable, parallel distributed query approach that is offered via FedShard. The new release includes support for the RDF* and SPARQL* extensions and extended support for SHACL (SHApe Constraint Language).