Supporting ranking pattern-based aggregate queries in sequence data cubes

Chun Kit Chui, Eric Lo, Ben Kao, Wai-Shing Ho

Abstract: Sequence data processing has been studied extensively in the literature. In recent years, the warehousing and online-analytical processing (OLAP) of archived sequence data have received growing attentions. In particular, the concept of sequence OLAP is recently proposed with the objective of evaluating various kinds of so-called Pattern-Based Aggregate (PBA) queries so that various kinds of data analytical tasks on sequence data can be carried out efficiently. This paper studies the evaluation of ranking PBA queries, which rank the results of PBA queries and return only the top-ranked ones to users. We discuss how ranking PBA queries drastically improve the usability of S-OLAP systems and present techniques that can evaluate various kinds of ranking PBA queries efficiently.