Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments are described for a method of distributing n-tuples over a cluster of triple-store machines, by storing each n-tuple as text in a distributed file system using a key value store; providing each machine of the cluster with a resident semantic data lake component accessing one or more persistent RDF triplestores for the n-tuple data stored on each machine; and defining one part of each n-tuple as a partition variable to ensure locality of data within each respective n-tuple. A method includes inserting graphs into a key/value store to determine how the key/value store distributes the data across a plurality of servers, by generating textual triple data, and storing the triple data in key-value stores wherein a fourth element of the triple comprises the key, and a value associated with the key comprises all the triples about a subject; indexing the data in the key-value store in an RDF triplestore using a partition based on the fourth element.

Abstract: Embodiments of a method for creating a graph database which is arranged to store data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (UPI). A geospatial UPI is defined for two-dimensional data elements that include a plurality of data points defined by respective pairs of an X-ordinate and a Y-ordinate. A two-dimensional data space is divided into a plurality of uniform Y strips of defined widths. The Y-strip width is selected to correspond approximately to an anticipated Y dimension of a typical search region of interest. The geospatial UPI encodes a plurality of coordinate points in a two -dimensional data space into a single datum that includes respective pairs of an X-ordinate and a Y-ordinate. The geospatial UPI comprises a data structure arranged in order of sort priority by the geospatial UPI type code, geospatial subtype, and Y-strip identifier followed by the X-ordinate data and Y-ordinate data.

Abstract: Embodiments of a method for creating a graph database which is arranged to store or process data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (hereinafter called a UPI), each UPI comprises a tag at a fixed position within the UPI. The tag indicates the datatype of the encoded tuple-part. The content data for the tuple-part is encoded in a code that is configured to reflect the ranking or order of the content data, corresponding to each datatype, relative to other tuples in a set of tuples. For content data that comprises a character-string, the code comprises a hashcode; and for content data that comprises or includes a numeric value, the code comprises an immediate value that directly stores the numeric value without encoding.