Herein are techniques that concurrently populate entries in a compressed sparse row (CSR) encoding, of a type of edge of a heterogenous graph. In an embodiment, a computer obtains a mapping of a relational schema to a graph data model. The relational schema defines vertex tables that correspond to vertex types in the graph data model, and edge tables that correspond to edge types in the graph data model. Each edge type is associated with a source vertex type and a target vertex type. For each vertex type, a sequence of persistent identifiers of vertices is obtained. Based on the mapping and for a CSR representation of each edge type, a source array is populated that, for a same vertex ordering as the sequence of persistent identifiers for the source vertex type, is based on counts of edges of the edge type that originate from vertices of the source vertex type. For the CSR, the computer populates, in parallel and based on said mapping, a destination array that contains canonical offsets as sequence positions within the sequence of persistent identifiers of the vertices.

Described are methods, systems and computer readable media for performance logging of complex query operations.

An example method is provided in according with one implementation of the present disclosure. The method comprises generating, via a processor, a set of hashes for each of a plurality of objects. The method also comprises computing, via the processor, a high-dimensional sparse vector for each object, where the vector represents the set of hashes for each object. The method further comprises computing, via the processor, a combined high-dimensional sparse vector from the high-dimensional sparse vectors for all objects and computing a hash suppression threshold. The method also comprises determining, via the processor, a group of hashes to be suppressed by using the hash suppression threshold, and suppressing, via the processor, the group of selected hashes when performing an action.

Described are methods, systems and computer readable media for data source refreshing.

One embodiment sets forth a technique for generating a tree structure within a computer memory for storing sparse data. The technique includes dividing a matrix into a first plurality of equally sized regions. The technique also includes dividing at least one region in the first plurality of regions into a second plurality of regions, where the second plurality of regions includes a first region and one or more second regions that have a substantially equal number of nonzero matrix values and are formed within the first region. The technique further includes creating the tree structure within the computer memory by generating a first plurality of nodes representing the first plurality of regions, generating a second plurality of nodes representing the second plurality of regions, and grouping, under a first node representing the first region, one or more second nodes representing the one or more second regions.

A third-party service may be used to assist entities in responding to requests of users. A third-party service may receive, directly or indirectly, a request of a first user for assistance from a first entity. The third-party service may request information about the first user by sending a request to a computer of the first entity. The third-party service may use the request of the first user and the information about the first user to automatically generate a response to the request of the first user. The third-party service may then transmit, directly or indirectly, the response to the first user.

There is provided a method of using a bitmap filter to filter a data source as part of a join operation. A set bit at a bit location of the bitmap filter is associated with an entry of the data source that comprises an identifier that matches a filter parameter. A row identifier of a row in another data source that corresponds to the identifier is determined based on a sum of a number of bits set in bit locations that are between the bit location of the set bit associated with the entry of the data source and a bit location in the bitmap filter corresponding to a different, known, row identifier of a different row in the another data source. The row of the another data source corresponding to the determined row identifier is interrogated to obtain information defining the identifier that matches the filter parameter.

An approach is provided for combining location data sources. The approach, for instance, involves generating a first context-aware vector representation of a first location entity in a first data source and a second context-aware vector representation of a second location entity in a second data source. The approach also comprises processing the first context-aware vector representation and the second context-aware vector representation using a machine learning model to perform a classification of the first location entity as the same as the second location entity. The approach further comprises combining the first data source and the data source into a new database based on the classification and providing the new database as an output.

A device for computing an inner product includes an index unit, a storage operation unit, a redundant to 2's complement (RTC) converter, a mapping table, and a multiplier-accumulate (MAC) module. The index unit, storing index values, is coupled to word lines. The storage operation unit includes the word lines and bit lines and stores data values. The mapping table stores coefficients corresponding to the index values. The index unit enables the word line according to a count value and the index value, such that the storage operation unit accumulates the data values corresponding to the bit lines and the enabled word line, thereby generating accumulation results. The RTC converter converts the accumulation results into a total data value in 2's complement format. The MAC module operates based on the total data value and the coefficient to generate an inner product value.

Methods and apparatus are disclosed to generate a recommendation, including an attribute vector aggregator to form a resultant attribute vector based on an input set of attribute vectors, the set of attribute vectors containing at least one of a media attribute vector, an attendee attribute vector, an artist attribute vector, an event attribute vector, or a venue attribute vector, and a recommendation generator, the recommendation generator including: a vector comparator to perform a comparison between an input attribute vector and other attribute vectors and a recommendation compiler to create one or more recommendations of at least one of media, an artist, an event, or a venue based on the comparison.