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Range Subsequence Matching under Dynamic Time Warping  

Han, Wook-Shin (경북대학교 컴퓨터공학과)
Lee, Jin-Soo (경북대학교 컴퓨터공학과)
Moon, Yang-Sae (강원대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.14, no.6, 2008 , pp. 559-566 More about this Journal
Abstract
In this paper, we propose a range subsequence matching under dynamic time warping (DTW) distance. We exploit Dual Match, which divides data sequences into disjoint windows and the query sequence into sliding windows. However, Dual Match is known to work under Euclidean distance. We argue that Euclidean distance is a fragile distance, and thus, DTW should be supported by Dual Match. For this purpose, we derive a new important theorem showing the correctness of our approach and provide a detailed algorithm using the theorem. Extensive experimental results show that our range subsequence matching performs much better than the sequential scan algorithm.
Keywords
Subsequence matching; DTW;
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  • Reference
1 Rafiei, D. et al., "Querying Time Series Data Based on Similarity," IEEE TKDE, Vol.12, No.5, 2000
2 Itakura, F., "Minimum Prediction Residual Principle Applied to Speech Recognition," IEEE Trans. on ASSP, Vol. ASSP-23, No.1, pp. 67-72, 1975
3 Berchtold, S., Bohm, C., and Kriegel, H.-P., "The Pyramid-Technique: Towards Breaking the Curse of Dimensionality," In SIGMOD, pp. 142-153, 1998   DOI   ScienceOn
4 Weber, R. et al., "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces," In VLDB, pp. 194- 205, pp. 194-205, Aug. 1998
5 Moon, Y.-S., Whang, K.-Y., and Han, W.-S., "General Match: A Subsequence Matching Method in Time-Series Databases Based on Generalized Windows," In SIGMOD, 2002
6 Berndt, D. and Clifford, J., "Finding Patterns in Time Series: a Dynamic Programming Approach," AAAI/MIT, 1996
7 Bartolini, I., Ciaccia, P., and Patella, M., "WARP: Accurate Retrieval of Shapes Using Phase of Fourier Descriptors and Time Warping Distance," IEEE PAMI, pp. 142-147, 2005   DOI   ScienceOn
8 Yi, B.-K. and Faloutsos, C., "Fast Time Sequence Indexing for Arbitrary Lp Norms," In VLDB, pp. 385-394, 2000
9 Han, W.-S. et al., "Ranked subsequence matching in time-series databases," In VLDB, pages 423-434 2007
10 Lee, J., Han W.-S., and Moon Y.-S., "Range Search under Dynamic Time Warping," In APIS, pp. 67-70, 2007
11 Zhu, Y. and Shasha, D., "Warping Indexes with Envelope Transforms for Query by Humming," In SIGMOD, 2003
12 Beckmann, N. et al., "The R*-tree: An Efficient and Robust Access Method for Points and Rectangles," In SIGMOD, pp. 322-331, 1990
13 Moon, Y.-S., Whang, K.-Y., and Loh, W.-K., "Duality-Based Subsequence Matching in Time- Series Databases," In ICDE, pp. 263-272, 2001
14 Agrawal, R., Faloutsos, C., and Swami, A., "Efficient Similarity Search in Sequence Databases," In FODO, 1993
15 Sobell, M. O., Practical Guide to Linux Commands, Editors, and Shell Programming, Prentice Hall, 2005
16 Keogh, E., "Exact indexing of dynamic time warping," In VLDB, pp 406-417, 2002
17 Faloutsos, C. et al., "Fast Subsequence Matching in Time-Series Databases," In SIGMOD, 1994
18 Sakoe, H. and Chiba, S., "Dynamic Programming Algorithm Optimization for Spoken Word Recognition," IEEE ASSP, 1978
19 Keogh, E., "A Decade of Progress in Indexing and Mining Large Time Series Databases," In VLDB, Tutorial, 2006
20 Rabiner, L and Juang, B., Fundamentals of Speech Recognition, Englewood Cliffs, N. J., 1993