Supporting Interactive Sequential Pattern Discovery in Databases

One of the most important data mining problems is discovery of sequential patterns. Sequential pattern mining consists in discovering all frequently occurring subsequences in a collection of data sequences. This paper discusses several issues concerning possible extensions to traditional database management systems required to support sequential pattern discovery: a sequential pattern query language for specifying mining tasks and storing discovered patterns in the database, techniques of integrating various pattern constraints that can be specified in mining queries into the mining process in order to improve performance, and a framework for exploiting cached results of previous queries to support iterative and interactive data mining. The paper summarizes the author’s recent research on the above topics.