ARCube: Supporting Ranking Aggregate Queries in Partially Materialized Data Cubes

Supporting ranking queries in database systems has been a popular research topic recently. However, there is a lack of study on supporting ranking queries in data warehouses where ranking is on multidimensional aggregates instead of on measures of base facts. To address this problem, we pro- pose a query execution model to answer di®erent types of ranking aggregate queries based on a uni¯ed, partial cube structure, ARCube. The query execution model follows a candidate generation and veri¯cation framework, where the most promising candidate cells are generated using a set of high-level guiding cells. We also identify a bounding princi- ple for e®ective pruning: once a guiding cell is pruned, all of its children candidate cells can be pruned. We further address the problem of e±cient online candidate aggrega- tion and veri¯cation by developing a chunk-based execution model to verify a bulk of candidates within a bounded mem- ory bu®er. Our extensive performance study shows that the new framework not only leads to an order of magnitude performance improvements over the state-of-the-art method, but also is much more °exible in terms of the types of rank- ing aggregate queries supported.
Date: June 02, 2008
Book Title: ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'08
Type: InProceedings
Edition: Proc. 2008
Address: Vancouver, Canada
Downloads: 380

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Bibtex


@InProceedings{ARCube_Supporting_Ranking_Aggregate_Quer,
  author = "Tianyi Wu and Dong Xin and Jiawei Han",
  title = "{ARCube: Supporting Ranking Aggregate Queries in Partially Materialized Data Cubes}",
  month = "June",
  year = "2008",
  edition = "Proc. 2008",
  address = ", Vancouver, Canada",
  booktitle = "ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'08",
}