Discovery of frequent patterns is a very important data mining problem with numerous applications. Frequent pattern mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on efficient processing of frequent pattern queries has been done in recent years, focusing mainly on constraint handling and reusing results of previous queries. In this paper we tackle the problem of optimizing a sequence of frequent pattern queries, submitted to the system as a batch. Our solutions are based on previously proposed techniques of reusing results of previous queries, and exploit the fact that knowing a sequence of queries a priori gives the system a chance to schedule and/or adjust the queries so that they can use results of queries executed earlier. We begin with simple query scheduling and then consider other transformations of the original batch of queries.
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