A Recommendation System for Execution Plans Using Machine Learning
LRIT-CNRST (URAC No. 29), Faculty of Sciences, Mohammed V University in Rabat, Rabat 10000, Morocco
Team TIM, High School of Technology, Moulay Ismail University in Meknes, Meknes 50050, Morocco
Author to whom correspondence should be addressed.
Academic Editor: Mehmet Ali Ilgın
Received: 13 April 2016 / Revised: 6 June 2016 / Accepted: 7 June 2016 / Published: 15 June 2016
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
of the cases.
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MDPI and ACS Style
Zahir, J.; El Qadi, A. A Recommendation System for Execution Plans Using Machine Learning. Math. Comput. Appl. 2016, 21, 23.
Zahir J, El Qadi A. A Recommendation System for Execution Plans Using Machine Learning. Mathematical and Computational Applications. 2016; 21(2):23.
Zahir, Jihad; El Qadi, Abderrahim. 2016. "A Recommendation System for Execution Plans Using Machine Learning." Math. Comput. Appl. 21, no. 2: 23.
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