Abstract
Dynamic Time Warping (DTW) has been widely used for measuring the distance between the two time series, but its computational complexity is too high to be directly applied to similarity search in large databases. In this paper, we propose a new approach to deal with this problem. It builds the filtering process based on histogram distance, using mean value to mark the trend of points in every segment and counting different binary bits to select the candidate sequences. Therefore, it produces a more appropriate collection of candidates than original binary histograms in less time, guaranteeing no false dismissals. The results of simulation experiments prove us that the new method exceeds the original one.
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
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAI-94 Workshop on Knowledge Discovery in Databases (KDD), New York, USA, pp. 229–248 (1994)
Ratanamahatana, C.A., Keogh, E.: Three myths about dynamic time warping data mining. In: SIAM Conference on Data Mining (SDM), Newport Beach, USA, pp. 506–510 (2005)
Yi, B.K., Jagadish, H.V., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: 14th International Conference on Data Engineering (ICDE), Orlando, USA, pp. 201–208 (1998)
Park, S., Chu, W., Yoon, J., Hsu, C.: Efficient searches for similarity subsequences of different lengths in sequence databases. In: 16th International Conference on Data Engineering (ICDE), Los Angeles, USA, pp. 23–32 (2000)
An, J.Y., Chen, H.X., Furuse, K., Ohbo, N., Keogh, E.: Grid-based indexing for large time series databases. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 614–621. Springer, Heidelberg (2003)
Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowledge and Information Systems 7, 358–386 (2005)
Ruengronghirunya, P., Niennattrakul, V., Ratanamahatana, C.: Speeding up similarity search on a large time series dataset under time warping distance. In: 13th Pacific-Asia Conference on Knowledge and Data Mining (PAKDD), Bangkok, Thailand, pp. 981–988 (2009)
Fu, A.W.C., Keogh, E., Lau, L.Y.H., Ratanamahatana, C.A., Wong, R.C.W.: Scaling and time warping in time series querying. VLDB Journal 17, 899–921 (2008)
Lemire, D.: Faster retrieval with a two-pass dynamic-time-warping lower bound. Pattern Recognition 42, 2169–2180 (2009)
Aβfalg, J., Kriegel, H., Kröger, P., Renz, M.: Probabilistic similarity search for uncertain time series. In: 21st International Conference on Scientific and Statistical Database Management (SSDBM), New Orleans, USA, pp. 435–443 (2009)
Kim, S.W., Park, S., Chu, W.W.: An index-based approach for similarity search supporting time warping in large sequence databases. In: 17th International Conference on Data Engineering (ICDE), Heidelberg, Germany, pp. 607–614 (2001)
Gu, J., Jin, X.M.: A simple approximation for dynamic time warping search in large time series database. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 841–848. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ouyang, Y., Zhang, F. (2010). Histogram Distance for Similarity Search in Large Time Series Database. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-15381-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15380-8
Online ISBN: 978-3-642-15381-5
eBook Packages: Computer ScienceComputer Science (R0)