Efficient computation of Iceberg cubes with complex measures

  • Authors:

  • Jiawei Han;Jian Pei;Guozhu Dong;Ke Wang

  • Affiliations:

  • School of Computing Science, Simon Fraser University, B.C., Canada;School of Computing Science, Simon Fraser University, B.C., Canada;Department of Computer Science, Wright State University, Dayton, OH;School of Computing Science, Simon Fraser University, B.C., Canada

  • Venue:
  • SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
  • Year:
  • 2001

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Abstract

It is often too expensive to compute and materialize a complete high-dimensional data cube. Computing an iceberg cube, which contains only aggregates above certain thresholds, is an effective way to derive nontrivial multi-dimensional aggregations for OLAP and data mining.In this paper, we study efficient methods for computing iceberg cubes with some popularly used complex measures, such as average, and develop a methodology that adopts a weaker but anti-monotonic condition for testing and pruning search space. In particular, for efficient computation of iceberg cubes with the average measure, we propose a top-k average pruning method and extend two previously studied methods, Apriori and BUC, to Top-k Apriori and Top-k BUC. To further improve the performance, an interesting hypertree structure, called H-tree, is designed and a new iceberg cubing method, called Top-k H-Cubing, is developed. Our performance study shows that Top-k BUC and Top-k H-Cubing are two promising candidates for scalable computation, and Top-k H-Cubing has better performance in most cases.