Exploiting hierarchical clustering in evaluating multidimensional aggregation queries

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

Multidimensional aggregation queries constitute the single most important class of queries for data warehousing applications and decision support systems. The bottleneck in the evaluation of these queries is the join of the usually huge fact table with the restricted dimension tables (star-join). Recently, a multidimensional hierarchical clustering schema for star schemas is suggested. Subsequently, query evaluation plans for multidimensional queries appeared that essentially implement a star join as a multidimensional range restriction. We present a number of transformations for such plans. The transformations place grouping/aggregation operations before joins and safely prune aggregated tuples. They can be applied at no or minimal extra I/O cost. We show how these transformations can be used to construct a new evaluation plan for grouping/aggregation queries over multidimensional hierarchically clustered schemas. The new plan improves previous results by grouping and aggregating tuples and by excluding aggregated tuples from further consideration at an early stage of the computation of a query.

Original languageEnglish (US)
Pages63-70
Number of pages8
DOIs
StatePublished - 2003
EventDOLAP 2003: Proceedings of the Sixth ACM International Workshop on Data Warehousing and OLAP - New Orleans, LA, United States
Duration: Nov 7 2003Nov 7 2003

Other

OtherDOLAP 2003: Proceedings of the Sixth ACM International Workshop on Data Warehousing and OLAP
Country/TerritoryUnited States
CityNew Orleans, LA
Period11/7/0311/7/03

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Multidimensional aggregation query
  • Multidimensional hierarchical clustering
  • Query transformations
  • Star join

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