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The power-method: a comprehensive estimation technique for multi-dimensional queries
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Source Conference on Information and Knowledge Management archive
Proceedings of the twelfth international conference on Information and knowledge management table of contents
New Orleans, LA, USA
SESSION: Database session 2: querying high-dimensional data II table of contents
Pages: 83 - 90  
Year of Publication: 2003
ISBN:1-58113-723-0
Authors
Yufei Tao  City University of Hong Kong, Hong Kong
Christos Faloutsos  Carnegie Mellon University, Pittsburgh, PA
Dimitris Papadias  HKUST, Clear Water Bay, Hong Kong
Sponsors
ACM: Association for Computing Machinery
SIGMIS: ACM Special Interest Group on Management Information Systems
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

Existing estimation approaches for multi-dimensional databases often rely on the assumption that data distribution in a small region is uniform, which seldom holds in practice. Moreover, their applicability is limited to specific estimation tasks under certain distance metric. This paper develops the Power-method, a comprehensive technique applicable to a wide range of query optimization problems under various metrics. The Power-method eliminates the local uniformity assumption and is accurate even in scenarios where existing approaches completely fail. Furthermore, it performs estimation by evaluating only one simple formula with minimal computational overhead. Extensive experiments confirm that the Power-method outperforms previous techniques in terms of accuracy and applicability to various optimization scenarios.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Yufei Tao: colleagues
Christos Faloutsos: colleagues
Dimitris Papadias: colleagues