Multi-tenant knowledge graph databases with dynamic specification and enforcement of ontological data models

A system for cybersecurity analysis utilizing high-performance, scalable, multi-tenant, dynamically specifiable, knowledge graph based information storage and utilization, comprising:

• a computing device comprising a processor and a memory;
• a distributed in-memory associative array comprising key-value pairs stored in the memory of a plurality of networked computing devices and configured to represent a knowledge graph;
• an integration platform comprising a first plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
• compile a received schema into data types, each data type comprising an entity definition, one or more link definitions, and one or more attribute definitions;
• send the compiled schema to a cloud-based knowledge graph service, wherein the cloud-based knowledge graph service instantiates a knowledge graph;
• retrieve the knowledge graph from the knowledge graph service and implement the knowledge graph in the distributed in-memory associative array;
• receive one or more data clumps, each data clump comprising entity, link, and attribute data;
• for each received data clump, determine whether each data clump complies with the schema;
• send each compliant data clump to the cloud-based knowledge graph service engine for assembly into a new knowledge graph that is created to be fully compliant with the schema, wherein the cloud-based knowledge graph service:
• receives data clumps from the integration platform wherein the data clumps are known to be fully compliant with the schema;
• and
• populates the knowledge graph in the distributed in-memory associative array with the entity, link, and attribute data from each fully-compliant data clump to generate a new fully-compliant knowledge graph;
• and
• retrieve the populated knowledge graph from the knowledge graph service and update the distributed in-memory associative array with the populated knowledge graph.

A method for system for cybersecurity analysis utilizing high-performance, scalable, multi-tenant, dynamically specifiable, knowledge graph-based information storage and utilization, comprising the steps of:

• instantiating a distributed in-memory associative array comprising key-value pairs stored in the memory of a plurality of networked computing devices and configured to represent a knowledge graph;
• compiling a received schema into data types, each data type comprising an entity definition, one or more link definitions, and one or more attribute definitions;
• sending the compiled schema to a cloud-based knowledge graph service, wherein the cloud-based knowledge graph service
• instantiates a knowledge graph;
• retrieving the knowledge graph from the knowledge graph service and implementing the knowledge graph in the distributed in-memory associative array;
• receiving one or more data clumps, each data clump comprising entity, link, and attribute data;
• determining whether each data clump complies with the schema;
• enforcing compliance of each data clump by accepting compliant data clumps and rejecting non-compliant data clumps;
• sending each compliant data clump to the cloud-based knowledge graph service, wherein the cloud-based knowledge graph service
• populates the knowledge graph in the distributed in-memory associative array with the entity, link, and attribute data in each data clump to generate a new fully-compliant knowledge graph;
• and
• retrieving the populated knowledge graph from the knowledge graph service and updating the distributed in-memory associative array with the populated knowledge graph.