University of Illinois Urbana-Champaign

Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP

Li, Chengkai; Chang, Kevin Chen-Chuan; Ilyas, Ihab F.

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Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP.pdf

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https://hdl.handle.net/2142/11065

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Title
Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP
Author(s)
  • Li, Chengkai
  • Chang, Kevin Chen-Chuan
  • Ilyas, Ihab F.
Issue Date
2005-06
Keyword(s)
Web search; database systems
Date of Ingest
2009-04-20T15:06:47Z
Abstract
In this paper, we develop a principled framework for efficient processing of ad-hoc top-k (ranking) aggregate queries in OLAP. Such queries provide the k groups with the highest aggregates to decision makers. Essential support of top-k aggregate queries is lacking in current RDBMSs, which process such queries in a naive and overkill materialize- group-sort scheme, therefore can be prohibitively inefficient. Our new framework is based on two fundamental properties, the Group-Ranking and Tuple-Ranking Principles. The principles dictate group-ordering and tuple-ordering requirement that together guide the query processor toward the optimal aggregate query processing. To realize the requirements, we propose a new execution model and address the challenges of implementing new query operators, enabling efficient top-k aggregate query plans that are both group- aware and rank-aware. The experimental study validates our framework by demonstrating orders of magnitude performance improvement in the new query plans, compared with the traditional approach.
Type of Resource
text
Genre of Resource
Technical Report
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http://hdl.handle.net/2142/11065
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