Zahir, J. and El Qadi, A. (2016) A recommendation system for execution plans using machine learning. Mathematical and Computational Applications, 21 (2).

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Generating execution plans is a costly operation for the DataBase Management System (DBMS). An interesting alternative to this operation is to reuse the old execution plans, that were already generated by the optimizer for past queries, to execute new queries. In this paper, we present an approach for execution plan recommendation in two phases. We firstly propose a textual representation of our SQL queries and use it to build a Features Extractor module. Then, we present a straightforward solution to identify query similarity.This solution relies only on the comparison of the SQL statements. Next, we show how to build an improved solution enabled by machine learning techniques. The improved version takes into account the features of the queries' execution plans. By comparing three machine learning algorithms, we find that the improved solution using Classification Based on Associative Rules (CAR) identifies similarity in 91 of the cases. © 2016 by the authors; licensee MDPI, Basel, Switzerland.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence; Database systems; Learning systems; Online systems; Query processing; Railroad cars, Associative rule; Execution plans; Machine learning techniques; Optimizers; Recommendation; SQL query; SQL statements; Textual representation, Learning algorithms
Subjects: Engineering
Divisions: SCIENTIFIC PRODUCTION > Engineering
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:47
URI: http://eprints.umi.ac.ma/id/eprint/3258

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