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General match: a subsequence matching method in time-series databases based on generalized windows
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Source International Conference on Management of Data archive
Proceedings of the 2002 ACM SIGMOD international conference on Management of data table of contents
Madison, Wisconsin
SESSION: Research sessions: similarity and matching table of contents
Pages: 382 - 393  
Year of Publication: 2002
ISBN:1-58113-497-5
Authors
Yang-Sae Moon  Korea Advanced Institute of Science and Technology (KAIST), Taejon, Korea
Kyu-Young Whang  Korea Advanced Institute of Science and Technology (KAIST), Taejon, Korea
Wook-Shin Han  Korea Advanced Institute of Science and Technology (KAIST), Taejon, Korea
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 11,   Downloads (12 Months): 74,   Citation Count: 19
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ABSTRACT

We generalize the method of constructing windows in subsequence matching. By this generalization, we can explain earlier subsequence matching methods as special cases of a common framework. Based on the generalization, we propose a new subsequence matching method, General Match. The earlier work by Faloutsos et al. (called FRM for convenience) causes a lot of false alarms due to lack of point-filtering effect. Dual Match, recently proposed as a dual approach of FRM, improves performance significantly over FRM by exploiting point filtering effect. However, it has the problem of having a smaller allowable window size---half that of FRM---given the minimum query length. A smaller window increases false alarms due to window size effect. General Match offers advantages of both methods: it can reduce window size effect by using large windows like FRM and, at the same time, can exploit point-filtering effect like Dual Match. General Match divides data sequences into generalized sliding windows (J-sliding windows) and the query sequence into generalized disjoint windows (J-disjoint windows). We formally prove that General Match is correct, i.e., it incurs no false dismissal. We then propose a method of estimating the optimal value of the sliding factor J that minimizes the number of page accesses. Experimental results for real stock data show that, for low selectivities (10-6∼10-4), General Match improves average performance by 117% over Dual Match and by 998% over FRM; for high selectivities (10-3∼10-1), by 45% over Dual Match and by 64% over FRM. The proposed generalization provides an excellent theoretical basis for understanding the underlying mechanisms of subsequence matching.


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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K. Whang, G. Wiederhold, and D. Sagalowicz. Separability-an approach to physical database design. IEEE Trans. on Computers, c-33(3):209-222, 1984.
 
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A. C.-C. Yao. On random 2-3 trees. Acta Informatica, 9:159-170, 1978.
 
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CITED BY  19

Collaborative Colleagues:
Yang-Sae Moon: colleagues
Kyu-Young Whang: colleagues
Wook-Shin Han: colleagues