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Automatic physical database tuning: a relaxation-based approach
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Source International Conference on Management of Data archive
Proceedings of the 2005 ACM SIGMOD international conference on Management of data table of contents
Baltimore, Maryland
SESSION: Research papers: adaptive, automatic, autonomic systems table of contents
Pages: 227 - 238  
Year of Publication: 2005
ISBN:1-59593-060-4
Authors
Nicolas Bruno  Microsoft Research
Surajit Chaudhuri  Microsoft Research
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 20,   Downloads (12 Months): 123,   Citation Count: 13
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ABSTRACT

In recent years there has been considerable research on automated selection of physical design in database systems. In current solutions, candidate access paths are heuristically chosen based on the structure of each input query, and a subsequent bottom-up search is performed to identify the best overall configuration. To handle large workloads and multiple kinds of physical structures, recent techniques have become increasingly complex: they exhibit many special cases, shortcuts, and heuristics that make it very difficult to analyze and extract properties. In this paper we critically examine the architecture of current solutions. We then design a new framework for the physical design problem that significantly reduces the assumptions and heuristics used in previous approaches. While simplicity and uniformity are important contributions in themselves, we report extensive experimental results showing that our approach could result in comparable (and, in many cases, considerably better) recommendations than state-of-the-art commercial alternatives.


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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S. Agrawal, S. Chaudhuri, L. Kollar, A. Marathe, V. Narasayya, and M. Syamala. Database Tuning Advisor for Microsoft SQL Server 2005. In Proceedings of the 30th International Conference on Very Large Databases, 2004.
 
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B. Dageville, D. Das, K. Dias, K. Yagoub, M. Zait, and M. Ziauddin. Automatic SQL Tuning in Oracle 10g. In Proceedings of the 30th International Conference on Very Large Databases, 2004.
 
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G. Graefe. The Cascades framework for query optimization. Data Engineering Bulletin, 18(3), 1995.
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D. Zilio, J. Rao, S. Lightstone, G. Lohman, A. Storm, C. Garcia-Arellano, and S. Fadden. DB2 design advisor: Integrated automatic physical database design. In Proceedings of the 30th International Conference on Very Large Databases, 2004.
 
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CITED BY  13
Collaborative Colleagues:
Nicolas Bruno: colleagues
Surajit Chaudhuri: colleagues