Microsoft Technology Licensing, LLC

Matteo Interlandi

Rathijit Sen

Carlo Aldo Curino

Marius Dumitru

Konstantinos Karanasos

Markus Weimer

Hans C Lehnert Merino

Yuan Yu

Subramaniam Venkatraman Krishnan

Raghunath Ramakrishnan

Kwanghyun Park

Saeed Amizadeh

Fotios Psallidas

Dalitso Hansini Banda

Jesus Camacho Rodriguez

Ivan Popivanov

Doris Suiyi Xin

Karla Jean Saur

Byung-Gon Chun

Gyeongin Yu

Krystian Sakowski

Zezhou Huang

Dong He

Supun Chathurang Nakandala

Example aspects include techniques for query processing over deep neural network runtimes. These techniques may include receiving a query including one or more query operators and determining a query representation based on the one or more query operators. In addition, the techniques may include determining a neural network program based on the query representation, the neural network program including one or more neural network operators for performing the query in a neural network runtime, generating a neural network data structure based on a dataset associated with the query, and executing the neural network program in the neural network runtime over the neural network data structure to generate a query result.

Method and system for query processing over tensor runtimes

A method, computer program product, and computing system for optimizing query operations on run length encoding (RLE) data in a parallel processing computing system. Data is received in a plurality of columns of an input table of a parallel processing computing system for query execution; the system determines that at least a portion of the received data in a first number of columns is compressed according to run length encoding (RLE), thereby comprising RLE data columns including RLE data and that the received data in a second number of columns is not compressed according to run length encoding (RLE), thereby comprising non-RLE data columns including non-RLE data. A query operation is executed on the RLE data and the non-RLE data by prioritizing processing of the RLE data columns over processing of the non-RLE data columns.

System and method for accelerating query execution

A method, computer program product, and computing system for processing query operations on run length encoding (RLE), data in a parallel processing computing system. Data for query execution is received at a parallel processing computing system, at least a portion of the data being compressed according to RLE, thereby forming RLE data; and a query operation is executed on the RLE data without performing a decompression operation on the RLE data.

System and method for performing query operations on run length encoded data

The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

Query processing with machine learning

Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training. Some examples determine whether an operator has multiple corresponding NN modules within the translation dictionary and make an optimized selection.

Neural adapter for classical machine learning (ML) models

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Matteo Interlandi