Abstract
Similarity search in large time series databases is an interesting and challenging problem. Because of the high dimensional nature of the data, the difficulties associated with dimensionality curse arise. The most promising solution is to use dimensionality reduction, and construct a multi-dimensional index structure for the reduced data. In this work we introduce a new approach called grid-based Datawise Dimensionality Reduction(DDR) which attempts to preserve the characteristics of time series. We then apply quantization to construct an index structure. An experimental comparison with existing techniques demonstrate the utility of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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 strucrture for high-dimensional space. In: Proc. of the 13th Australasian Database Conference, pp. 33–40 (2002)
An, J., Chen, H., Furuse, K., Ishikawa, M., Ohbo, N.: A vector-wise dimensionality reduction for indexing high dimensional data. In: Proc. of Pan-Yellw-Sea International Workshop on Information Technologies for Network Era, pp. 135– 142 (2002)
Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree:an efficient and robust access method for points and rectangles. In: Proceedings of the 1998 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)
Chen, H., An, J., Furuse, K., Ohbo, N.: C2VA:trim high dimensional indexes. In: Meng, X., Su, J., Wang, Y. (eds.) WAIM 2002. LNCS, vol. 2419, pp. 303–315. Springer, Heidelberg (2002)
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)
Katayama, N., Satoh, S.: The sr-tree: An index structure for high-dimensional nearest neighbor queries. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp. 369–380 (1997)
Keogh, E.J., 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.J., 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
Weber, R., Schek, H.J., 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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
An, J., Chen, H., Furuse, K., Ohbo, N., Keogh, E. (2003). Grid-Based Indexing for Large Time Series Databases. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_83
Download citation
DOI: https://doi.org/10.1007/978-3-540-45080-1_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
eBook Packages: Springer Book Archive