Micro Focus Lcc

Mahashweta Das

William Kevin Wilkinson

Alkis Simitsis

Alkiviadis Simitsis

Mehmet Kivanc Ozonat

Alexander Kalinin

Kevin Wilkinson

A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Processing data between data stores

Recommending analytic tasks based on similarity of datasets is disclosed. One example is a system including a data processor, a matching module, and a recommendation module. The data processor receives an incoming dataset via a processing system, and generates a feature vector for the incoming dataset. The matching module determines similarity measures between the generated feature vector and representative feature vectors for a plurality of datasets in a data repository, and selects at least one dataset of the plurality of datasets based on the similarity measures. The recommendation module identifies at least one analytic task associated with the selected dataset, and recommends, to a computing device via the processing system, the at least one analytic task to be performed on the incoming dataset.

Recommending analytic tasks based on similarity of datasets

Examples for mapping a relational database to a graph database include a mapping engine to execute an arbitrary query on a relational database, identify a result column tag based on a tag of an underlying base table, process the result column into a typed, directed property graph based on the result column tag, and output the typed, directed property graph to a graph database. Examples for mapping a graph database to a relational database include processing a graph transaction by updating a mapping layer with a surrogate describing a change to a database object, determining, for an object in the mapping layer, if a database constraint defined on the object is satisfied, collecting database changes defined by the surrogate into a database change request, submitting the change request to a relational database as a transaction, and deleting the surrogate for the object in the mapping layer.

Graph database and relational database mapping

Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.

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Palo Alto, CA, United States of America

Mahashweta Das