This paper addresses the problem of query-aware data cleaning in the context of a user query. In particular, we develop a novel Query-Driven Approach ( ${\tt QDA}$ ) that systematically exploits the semantics of the predicates in ${\tt SQL}$ -like selection queries to reduce the data cleaning overhead. The objective of ${\tt QDA}$ is to issue the minimum number of cleaning steps that are necessary to answer a given ${\tt SQL}$ -like selection correctly. The comprehensive empirical evaluation of ${\tt QDA}$ demonstrates outstanding results—that is ${\tt QDA}$ is significantly better compared to traditional ER techniques, especially when the query is very selective.