Efficient Computation of Iceberg Cubes with Complex Measures
Summary: Efficient iceberg-cube computation with complex measures (average) via a weaker anti-monotonic prune to shrink search space. Introduces Top-k Apriori/BUC and H-tree based Top-k H-Cubing; Top-k H-Cubing often yields best scalability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiawei Han (Simon Fraser University)
- 2. Jian Pei (Simon Fraser University)
- 3. Guozhu Dong (Wright State University)
- 4. Ke Wang (Simon Fraser University)
BibTeX Citation
@inproceedings{han_sigmod01,
title = {{Efficient Computation of Iceberg Cubes with Complex Measures}},
author = {Han, Jiawei and Pei, Jian and Dong, Guozhu and Wang, Ke},
series = {{SIGMOD} '01},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/375663.375664},
url = {https://dl.acm.org/doi/10.1145/375663.375664},
year = {2001}
}
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