Incremental maintenance of quotient cube for median

  • Authors:

  • Cuiping Li;Gao Cong;Anthony K. H. Tung;Shan Wang

  • Affiliations:

  • Renmin University of China, Beijing, China;Natl University of Singapore, S'pore, Singapore;Natl University of Singapore, S'pore, Singapore;Renmin University of China, Beijing, China

  • Venue:
  • Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2004

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Abstract

Data cube pre-computation is an important concept for supporting OLAP(Online Analytical Processing) and has been studied extensively. It is often not feasible to compute a complete data cube due to the huge storage requirement. Recently proposed quotient cube addressed this issue through a partitioning method that groups cube cells into equivalence partitions. Such an approach is not only useful for distributive aggregate functions such as SUM but can also be applied to the holistic aggregate functions like MEDIAN.Maintaining a data cube for holistic aggregation is a hard problem since its difficulty lies in the fact that history tuple values must be kept in order to compute the new aggregate when tuples are inserted or deleted. The quotient cube makes the problem harder since we also need to maintain the equivalence classes. In this paper, we introduce two techniques called addset data structure and sliding window to deal with this problem. We develop efficient algorithms for maintaining a quotient cube with holistic aggregation functions that takes up reasonably small storage space. Performance study shows that our algorithms are effective, efficient and scalable over large databases.