Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
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Data cubes facilitate fast On-Line Analytical Processing (OLAP). Iceberg cubes are special cubes comprise only the multi-dimensional groups satisfying some user-specified constraints. Previous algorithms have focused on iceberg cubes defined by relatively simple constraints such as "COUNT(*) ≥ δ" and "COUNT(*) ≥ δ AND AVG(Profit) ≥ α". We propose an algorithm I-Cubing that computes iceberg cubes defined by complex constraints involving multiple predicates of aggregates such as "COUNT(*) ≥ δ AND (AVG(Profit) ≥ α OR AVG(profit) ≤ β)". State-of-the-art iceberg cubing algorithms: BUC cannot handle such cases whereas H-Cubing has to incur extra cost. Our proposed bounding technique can prune for all the given constraints at once without extra cost. Experiments show that bounding has superior pruning power and I-Cubing is twice as fast as H-Cubing. Furthermore, I-Cubing performs equally well with more complex constraints.