Franz, a provider of semantic graph database technology for knowledge graph solutions, has introduced AllegroGraph 7. With this release, the patented distributed knowledge graph solution adds innovations that address the fact that large enterprises have knowledge graphs that are so large that no amount of vertical scaling will work. The solution allows infinite data integration by unifying all data and siloed knowledge into an entity-event knowledge graph solution that can support massive big data analytics.
“Large enterprises have knowledge graphs that are so big that no amount of vertical scaling will work,” said Jans Aasman, CEO of Franz. “When these organizations want to conduct new big data analytics, it requires a new effort by the IT department to gather semi-usable data for the data scientists, which can cost millions of dollars, waste valuable time and still not provide a holistic data architecture for querying across all data. ETL, Data Lakes and Property Graphs only exacerbate the problem by creating new data silos. AllegroGraph 7 takes a holistic approach to mixed data, unifying all enterprise data with domain knowledge, including taxonomies, ontologies and industry knowledge—making queries across all data possible, while simplifying and accelerating feature extraction for machine learning.”
AllegroGraph 7 includes five key capabilities:
Semantic Entity-Event Data Modeling: The Entity-Event Data Model utilized by AllegroGraph 7 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. Using this novel data model approach, organizations gain a holistic view of customers, patients, students or important entities and the ability to discover deep connections, uncover new patterns and attain explainable results.
FedShard Speeds Complex Queries: Through a patented in-memory federation function, the results from each machine are combined so that the query process appears as if only one database is being accessed, although many different databases and data stores and knowledgebases are actually being accessed and returning results. This unique data federation capability accelerates results for complex queries across highly distributed datasets and knowledgebases.
Large-scale Mixed Data Processing: The AllegroGraph 7 big data processing system is able to scale massive amounts of domain knowledge data by efficiently associating domain knowledge with partitioned data through shardable graphs on clusters of machines. AllegroGraph 7 efficiently combines partitioned data with domain knowledge through an innovative process that keeps as much of the data in RAM as possible to speed data access and fully utilize the processors of the query servers.
Browser-based Gruff: The query and visualization capabilities of Gruff, a knowledge graph visualization tool, are now available via a web browser and directly integrated in AllegroGraph 7. Gruff’s "Time Machine" provides users with the ability to explore temporal connections and see how relationships are created over time. Users can build visual graphs that display the relationships in graph databases, display tables of properties, manage queries, connect to SPARQL Endpoints, and build SPARQL or Prolog queries as visual diagrams. Gruff can be downloaded separately or is included with the AllegroGraph v7 distribution.
High-Performance Big Data Analytics: AllegroGraph 7 delivers high performance analytics by overcoming data processing issues related to disk versus memory access, uses processor core efficiency and updates domain knowledge databases across partitioned data systems in a highly efficient manner.
To learn more about Franz and AllegroGraph, go to www.franz.com.