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