Data mining is an interactive and iterative process. It is very likely that a user will execute a series of similar queries differing in pattern constraints and mining parameters, before he or she gets satisfying results. Unfortunately, data mining algorithms currently available suffer from long processing times, which is unacceptable in case of interactive mining. In this paper we discuss efficient processing of sequential pattern queries utilizing cached results of other sequential pattern queries. We analyze differences between sequential pattern queries and propose algorithms that in many cases can be used instead of time-consuming mining algorithms.