OLAP on sequence data

  • Authors:

  • Eric Lo;Ben Kao;Wai-Shing Ho;Sau Dan Lee;Chun Kit Chui;David W. Cheung

  • Affiliations:

  • The Hong Kong Polytechnic University, Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the 2008 ACM SIGMOD international conference on Management of data
  • Year:
  • 2008

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Abstract

Many kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. However, traditional online analytical processing (OLAP) systems and techniques were not designed for sequence data and they are incapable of supporting sequence data analysis. In this paper, we propose the concept of Sequence OLAP, or S-OLAP for short. The biggest distinction of S-OLAP from traditional OLAP is that a sequence can be characterized not only by the attributes' values of its constituting items, but also by the subsequence/substring patterns it possesses. This paper studies many aspects related to Sequence OLAP. The concepts of sequence cuboid and sequence data cube are introduced. A prototype S-OLAP system is built in order to validate the proposed concepts. The prototype is able to support "pattern-based" grouping and aggregation, which is currently not supported by any OLAP system. The implementation details of the prototype system as well as experimental results are presented.