Computing complex iceberg cubes by multiway aggregation and bounding

Chou, P and Zhang, X 2004, 'Computing complex iceberg cubes by multiway aggregation and bounding', in Y Kambayashi et al. (ed.) Data Warehousing and Knowledge Discovery: Sixth International Conference, Zaragoza, Spain, 8 November 2004, pp. 108-117.


Document type: Conference Paper
Collection: Conference Papers

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Title Computing complex iceberg cubes by multiway aggregation and bounding
Author(s) Chou, P
Zhang, X
Year 2004
Conference name International Conference on Data Warehousing and Knowledge Discovery
Conference location Zaragoza, Spain
Conference dates 8 November 2004
Proceedings title Data Warehousing and Knowledge Discovery: Sixth International Conference
Editor(s) Y Kambayashi et al.
Publisher Springer
Place of publication Berlin, Germany
Start page 108
End page 117
Total pages 10
Abstract Iceberg cubing is a valuable technique in data warehouses. The efficiency of iceberg cube computation comes from efficient aggregation and effective pruning for constraints. In advanced applications, iceberg constraints are often non-monotone and complex, for example, "Average cost in the range [51, 52] and standard deviation of cost less than beta". The current cubing algorithms either are efficient in aggregation but weak in pruning for such constraints, or can prune for non-monotone constraints but are inefficient in aggregation. The best algorithm of the former, Star-cubing, computes aggregations of cuboids simultaneously but its pruning is specific to only monotone constraints such as "COUNT(*) greater than or equal to delta". In the latter case, the Divide and Approximate pruning technique can prune for non-monotone constraints but is limited to bottom-up single-group aggregation. We propose a solution that exhibits both efficiency in aggregation and generality and effectiveness in pruning for complex constraints. Our bounding techniques are as general as the Divide and Approximate pruning techniques for complex constraints and yet our multiway aggregation is as efficient as Star-cubing.
Subjects Information and Computing Sciences not elsewhere classified
Keyword(s) aggregation
data warehousing
efficiency
iceberg cubing
DOI - identifier 10.1007/b99817
Copyright notice © Springer-Verlag Berlin Heidelberg 2004
ISBN 978-3-540-22937-7
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