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Query Optimization using Machine and Deep Learning
In this project we will investigate and develop novel techniques that employ Machine Learning (ML) and Deep Learning (DL) for the optimal execution of queries in a relational DBMS. The research will be conducted with close collaboration with the team of IBM that develops Db2, the well-known relational DBMS of IBM. The goal is to integrate the produced ‘learned’ optimizer in Db2. The students that will work on the project will extract information from queries and execution plans and use this information in order design, develop, and evaluate ML/DL-based approaches that learn the current optimizer’s cost inaccuracies. Furthermore, they will design a technique that enables the query optimizer to incorporate such information into the decision making process towards the selection of the best query execution plan.
Faculty Supervisor:
Vasiliki Kantere
Student:
Partner:
IBM Canada Ltd
Discipline:
Computer science
Sector:
Agriculture; Information and cultural industries; Manufacturing; Professional, scientific and technical services
University:
University of Ottawa
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.