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The QOL approach for optimizing distributed queries without complete knowledge

Abstract : This paper concerns the integration of the Case Based Reasoning (CBR) paradigm in query processing, providing a way to optimize queries when there is no prior knowledge on queried data sources and certainly no related metadata such as data statistics. Our Query Optimization by Learning (QOL) approach optimizes queries using cases generated from the evaluation of similar past queries. A query case comprises: (i) the query, (ii) the query plan and (iii) the measures (computational resources consumed) of the query plan. The work also concerns the way the CBR process interacts with the query plan generation process. This process uses classical heuristics and makes decisions randomly (e.g. when there is no statistics for join ordering and selection of algorithms, routing protocols); It also (re)uses cases (existing query plans) for similar queries parts, improving the query optimization and evaluation efficiency.
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https://hal.archives-ouvertes.fr/hal-00922886
Contributor : Etienne Dublé Connect in order to contact the contributor
Submitted on : Wednesday, January 1, 2014 - 4:00:55 PM
Last modification on : Thursday, October 21, 2021 - 3:47:45 AM

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Lourdes Martínez, Christine Collet, Christophe Bobineau, Etienne Dublé. The QOL approach for optimizing distributed queries without complete knowledge. IDEAS 2012 - International Database Engineering & Applications Sysmposium, Aug 2012, Prague, Czech Republic. pp.91-99, ⟨10.1145/2351476.2351487⟩. ⟨hal-00922886⟩

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