Microsoft Technology Licensing, LLC

Jesus Camacho Rodriguez

Yuanyuan Tian

Carlo Aldo Curino

Rathijit Sen

Konstantinos Karanasos

Tarique Ashraf Siddiqui

Jyoti Leeka

Dalitso Hansini Banda

Dong He

Supun Chathurang Nakandala

Rana Alotaibi

Hanxian Huang

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

Systems and methods for characterizing and forecasting evolving query workloads. The method includes receiving a query, the received query including a parameter value and an arrival time; identifying the query as a recurrent query; extracting a query template from the received query by parsing the received query; based at least on the identifying, generating a feature vector for the received query, the feature vector generated based on the extracted template and the parameter value; and forecasting a future query based on the generated feature vector by applying a neural network.

Characterizing and forecasting evolving query workloads

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Jesus Camacho Rodriguez