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How to evaluate multiple range-sum queries progressively
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Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems table of contents
Madison, Wisconsin
SESSION: Research session 4: query processing and optimization I table of contents
Pages: 133 - 141  
Year of Publication: 2002
ISBN:1-58113-507-6
Authors
Rolfe R. Schmidt  University of Southern California, Los Angeles, California
Cyrus Shahabi  University of Southern California, Los Angeles, California
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 25,   Citation Count: 2
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ABSTRACT

Users of decision support system typically submit batches of range-sum queries simultaneously rather than issuing individual, unrelated queries. We propose a wavelet based technique that exploits T/O sharing across a query batch to evaluate the set of queries progressively and efficiently. The challenge is that now controlling the structure of errors across query results becomes more critical than minimizing error per individual query. Consequently, we define a class of structural error penalty functions and show how they are controlled by our technique Experiments demonstrate that our technique is efficient as an exact algorithm, and the progressive estimates are accurate, even after less than one I/O per query.


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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R. R. Schmidt and C. Shahabi. Wavelet based density estimators for modeling OLAP data sets. In SIAM Workshop on Mining Scientific Datasets, Chicago, April 2001. Available at http://infolab.usc.edu/publication.html.
 
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Collaborative Colleagues:
Rolfe R. Schmidt: colleagues
Cyrus Shahabi: colleagues