Neo4j Sweden Ab

Navneet Mathur

Nir Avrahamov

Daniel Eric Monk

A system is disclosed to build and store a data model and queries for a graph database. In various embodiments, data defining one or more aspects of a data model associated with a graph database is received. A set of data that represents the data model is stored as a graph that includes one or more nodes to represent each of the following: data model metadata, one or more node labels comprising the graph database, one or more relationship types of the graph database, and one or more property definitions each associated with a property of at least one of a node label and a relationship type of the graph database.

Tool to build and store a data model and queries for a graph database

A graph-based data privacy compliance solution is disclosed. In various embodiments, a privacy graph database based at least in part on an associated privacy graph model is used to store private information of a person, including by including in the privacy graph database a node representing a critical data element comprising the private information of the person.

Graph-based data privacy compliance solution

Techniques are disclosed to provide a graph based recommendation system. A recommendation engine definition that includes for each of a plurality of pipeline phases a corresponding phase definition and data indicating a location of the phase in a pipeline defined by the recommendation engine definition is stored in a memory of other storage device. The recommendation engine definition is used to generate programmatically one or more procedures to provide a recommendation engine that implements the pipeline. An API usable by a client to obtain a recommendation from the recommendation engine is generated programmatically and exposed.

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Mountain View, CA, United States of America

Navneet Mathur