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Prefix-querying: an approach for effective subsequence matching under time warping in sequence databases
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Source Conference on Information and Knowledge Management archive
Proceedings of the tenth international conference on Information and knowledge management table of contents
Atlanta, Georgia, USA
Session: Sequence Mining table of contents
Pages: 255 - 262  
Year of Publication: 2001
ISBN:1-58113-436-3
Authors
Sanghyun Park  IBM T.J. Watson Research Center
Sang-Wook Kim  Kangwon National University
June-Suh Cho  IBM T.J. Watson Research Center
Sriram Padmanabhan  IBM T.J. Watson Research Center
Sponsors
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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Downloads (6 Weeks): 6,   Downloads (12 Months): 30,   Citation Count: 7
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ABSTRACT

This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In our earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multi-dimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality.In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments. The results reveal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.


REFERENCES

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Collaborative Colleagues:
Sanghyun Park: colleagues
Sang-Wook Kim: colleagues
June-Suh Cho: colleagues
Sriram Padmanabhan: colleagues