Abstract
Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree structures are quite weak, resulting in inefficient search. In this paper, we propose a dimensionality reduction method motivated by observations about the inherent character of each time series. A very compact index file is constructed. By scanning the index file, we can get a very small candidate set, so that the number of page access is dramatically reduced. We demonstrate the effectiveness of our approach on real and synthetic datasets.
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References
Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)
An, J., Chen, H., Furuse, K., Ishikawa, M., Ohbo, N.: The convex polyhedra technique: An index structure for high-dimensional space. In: Proc. of the 13th Australasian Database Conference, pp. 33–40 (2002)
An, J., Chen, H., Furuse, K., Ohbo, N., Keogh, E.: Grid-Based Indexing for Large Time Series Databases. In: 4th International Conference Intelligent Data Engineering and Automated Learning, pp. 614–621 (2003)
Beyer, K.S., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is “Nearest Neighbor” Meaningful. In: Proceeding s of 7th international conference on database theory (ICDT), pp. 217–235 (1999)
Beckmann, N., Kriegel, P.H., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, pp. 322–331 (1990)
Chakrabarti, K., Mehrotra, S.: Locally dimensionality reduction: A new approach to indexing high dimensional spaces. In: Proceedings of 26th International Conference on Very Large Data Bases, pp. 151–162 (2000)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic Discovery of Time Series Motifs. In: The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24-27, pp. 493–498 (2003)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pp. 419–429 (1994)
Guttman, A.: R-tree: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, pp. 47–57 (1984)
Hale, J.C., Sellars, H.L.: Compression of chemical process data by functional approximation and feature extraction. AIChE J. 42(2), 477 (1981)
Katayama, N., Satoh, S.: The SR-tree: An index structure for high-dimensional nearest neighbour queries. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp. 369–380 (1997)
Keogh, E., Lonardi, S., Chiu, W.: Finding Surprising Patterns in a Time Series Database In Linear Time and Space. In: The 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 23-26, pp. 550–556 (2002)
Keogh, E.: Exact Indexing of Dynamic Time Warping. In: Proceedings of 28th International Conference on Very Large Data Bases, pp. 406–417 (2002)
Keogh, E., Chakrabarti, K., Mehrotra, S., Pazzani, M.J.: Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pp. 151–162 (2001)
Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive. University of California - Computer Science & Engineering Department, Riverside CA (2002), http://www.cs.ucr.edu/~amonn/TSDMA/index.html
Keogh, E., Pazzani, M.J.: A simple dimensionality reduction technique for fast similarity search in large time series databases. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 122–133. Springer, Heidelberg (2000)
Moody, G.: Mit-bih database distribution. Cambridge, MA (2000), http://ecg.mit.edu/index.html
Seidl, T., Kriegel, H.P.: Optimal multi-step k-nearest neighbour queries. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp. 154–165 (1998)
Vlachos, M., Hadjieleftheriou, M., Gunopulos, D.: Indexing Multi- Dimensional Time-Series with Support for Multiple Distance Measures. In: The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24-27, pp. 216–225 (2003)
Weber, R., Schek, J.H., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proceedings of 24th International Conference on Very Large Data Bases, pp. 194–205 (1998)
Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary lp norms. In: Proceedings of 26th International Conference on Very Large Data Bases, pp. 385–394 (2000)
Yi, B.K., Jagadish, H.V., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: ICDE 2000, pp. 201–208 (2000)
Zhu, Y., Shasha, D.: Warping Indexes with Envelope Transforms for Query by Humming. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 181–192 (2003)
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An, J., Chen, YP.P., Keogh, E. (2004). A Grid-Based Index Method for Time Warping Distance. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_8
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DOI: https://doi.org/10.1007/978-3-540-27772-9_8
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