Sisense Ltd.

Nir Regev

Amir Orad

Adi Azaria

Guy Levy Yurista

A system and method for providing local approximations of query results are provided. The method includes querying a primary neural network with at least one test query, wherein the at least one test query includes a real test result derived from executing the at least one training query on a data set; receiving from the primary neural network a predicted test result in response to the at least one test query; sending, based on the predicted test result, a model of a primary neural network to a local machine; and storing the model of a local neural network of the local machine, wherein the local neural network is configured to generate a prediction in response to a user query received by the local machine.

System and method for approximating query results using local and remote neural networks

A system and method for representing query elements in an artificial neural network. A method includes generating a translation table based on a plurality of query elements, wherein the translation table maps a plurality of vectors to the plurality of query elements, wherein each of the plurality of vectors is mapped to at least one query element of the plurality of query elements, wherein a first vector of the plurality of vectors is mapped to at least two query elements of the plurality of query elements; converting a plurality of input query elements into respective numerical representations using the translation table; and generating a result for a database query based on the numerical representations.

System and method for representing query elements in an artificial neural network

A system and method for generating approximations of query results. The method includes sending a received query to a neural network, wherein the received query is executable on a target data set; receiving from the neural network a predicted result to the received query; providing the predicted result as a first output to a device having initiated the received query; determining a real result of the query from a data set stored in the database when the predicted result is insufficiently accurate; and providing the real result as a second output to a device having initiated the received query.

System and method for approximating query results using neural networks

A system and method for generating training sets for training neural networks. The method includes determining a segmentation based on a column from a columnar database table; generating a group-by query based on the segmentation; generating a plurality of reduced queries based on the group-by query; executing the group-by query on a table of a database to obtain a result table, wherein the result table includes a plurality of results, wherein each result corresponds to a respective reduced query of the plurality of reduced queries; and generating a plurality of training query pairs by pairing each reduced query with its corresponding reduced result.

System and method for generating training sets for neural networks

A system and method for representing query elements in an artificial neural network. The method includes generating a translation table based on a plurality of query elements, wherein the translation table maps a plurality of vectors to the plurality of query elements, wherein each of the plurality of vectors is mapped to at least one query element of the plurality of query elements, wherein a number of distinct query elements among the plurality of query elements is greater than a number of distinct vectors among the plurality of vectors.

A system and method for increasing the accuracy of generated approximations of query results. The method includes sending a received query to a first neural network and a second neural network; receiving from the first neural network a first predicted result to the received query; providing the first predicted result as a first output to a device having initiated the received query; receiving from the second neural network a second predicted result to the received query; and providing the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.

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Beit Guvrin, Israel

Nir Regev