International Business Machines Corporation

Guy Khazma

Paula Kim Ta-Shma

Yosef Moatti

Gal Lushi

A hub of a computing environment obtains a training set from an edge of the computing environment. The training set that is obtained includes data from the edge and is used to train a neural network at the hub. The training of the neural network provides a detector and a generator at the hub. A determination is made as to whether the training of the neural network is complete. Based on determining that the training of the neural network is complete, the detector is sent to the edge. The detector at the edge is to facilitate suppression of additional edge data to the hub based on the detector at the edge determining that the additional edge data is statistically similar, based on one or more selected criteria, to data used to train the neural network.

Reduction of data transmission and data storage using neural network technology

Techniques for data skipping that may be user defined and extensible may be provided. A method may include receiving a query, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, modifying, at the computer system, the received query to utilize at least one data skipping index to evaluate at least one criterion of the query, wherein the at least one data skipping index comprises an index on at least one attribute of data that excludes index information for at least a portion of those data items that do not meet the at least one criterion, and wherein the at least one data skipping index is generated based on information received from an application programming interface, and evaluating, at the computer system, the query.

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Ramat Gan, Israel

Guy Khazma