Abstract
Today, many scientific data sets are open to the public. For their owners, it is important to know what the information needs of the users are. In this paper, we study the problem of extracting and analyzing patterns from the query log of a database. We focus on design errors (antipatterns). Antipatterns do not only have a negative effect on query performance, they also might introduce bias on any subsequent analysis of the SQL log. We propose a framework to discover patterns and antipatterns in SQL query logs and to clean antipatterns. To study the usefulness of our approach and to reveal insights on antipatterns in logs of real-world systems, we examine the SQL log of the SkyServer project, with more than 40 million queries. Among the top 15 patterns, we found 6 antipatterns. Altogether, our results give way to the conclusion that antipatterns might falsify refactoring and any other downstream analyses.
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