skip to main content
10.1145/1065167.1065210acmconferencesArticle/Chapter ViewAbstractPublication PagespodsConference Proceedingsconference-collections
Article

FTW: fast similarity search under the time warping distance

Published: 13 June 2005 Publication History

Abstract

Time-series data naturally arise in countless domains, such as meteorology, astrophysics, geology, multimedia, and economics. Similarity search is very popular, and DTW (Dynamic Time Warping) is one of the two prevailing distance measures. Although DTW incurs a heavy computation cost, it provides scaling along the time axis. In this paper, we propose FTW (Fast search method for dynamic Time Warping), which guarantees no false dismissals in similarity query processing. FTW efficiently prunes a significant number of the search cost. Experiments on real and synthetic sequence data sets reveals that FTW is significantly faster than the best existing method, up to 222 times.

References

[1]
R. Agrawal, C. Faloutsos, and A. N. Swami. Efficient similarity search in sequence databases. In Proceedings of the 4th Conference on Foundations of Data Organization and Algorithms (FODO), pages 69--84, February 1993.
[2]
R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of VLDB, pages 490--501, September 1995.
[3]
N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: An efficient and robust access method for points and rectangles. In Proceedings of ACM SIGMOD, pages 322--331, May 1990.
[4]
S. Berchtold, C. Böhm, and H.-P. Kriegel. The pyramid-technique: Towards breaking the curse of dimensionality. In Proceedings of ACM SIGMOD, pages 142--153, June 1998.
[5]
D. J. Berndt and J. Clifford. Finding patterns in time series: A dynamic programming approach. In Advances in Knowledge Discovery and Data Mining, pages 229--248, AAAI/MIT, 1996.
[6]
S. Chu, E. Keogh, D. Hart, and M. Pazzani. Iterative deepening dynamic time warping for time series. In Proceedings of SIAM International Conference on Data Mining, 2002.
[7]
C. Faloutsos, H. V. Jagadish, A. O. Mendelzon, and T. Milo. A signature technique for similarity-based queries. In SEQUENCES, June 1997.
[8]
C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In Proceedings of ACM SIGMOD, pages 419--429, May 1994.
[9]
K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, 1990.
[10]
R. W. Hamming. Digital Filters. Englewood Cliffs, N. J., 1977.
[11]
K. J. Jacob and D. Shasha. Fintime --- a financial time series benchmark. http://cs.nyu.edu/cs/faculty/shasha/fintime.html, March 2000.
[12]
J.-S. R. Jang and H.-R. Lee. Hierarchical filtering method for content-based music retrieval via acoustic input. In Proceedings of ACM Multimedia, pages 401--410, September/October 2001.
[13]
H. Kawasaki, T. Yatabe, K. Ikeuchi, and M. Sakauchi. Automatic modeling of a 3d city map from real-world video. In Proceedings of ACM Multimedia (1), pages 11--18, October/November 1999.
[14]
E. J. Keogh. Exact indexing of dynamic time warping. In Proceedings of VLDB, pages 406--417, August 2002.
[15]
E. J. Keogh, K. Chakrabarti, S. Mehrotra, and M. J. Pazzani. Locally adaptive dimensionality reduction for indexing large time series databases. In Proceedings of ACM SIGMOD, pages 151--162, May 2001.
[16]
E. J. Keogh, K. Chakrabarti, M. J. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems, pages 263--286, 2000.
[17]
S.-W. Kim, S. Park, and W. W. Chu. An index-based approach for similarity search supporting time warping in large sequence databases. In Proceedings of ICDE, pages 607--614, April 2001.
[18]
Y.-S. Moon, K.-Y. Whang, and W.-S. Han. General match: a subsequence matching method in time-series databases based on generalized windows. In Proceedings of ACM SIGMOD, pages 382--393, June 2002.
[19]
D. W. Mount. Bioinfomatics: Sequence and Genome Analysis. Cold Spring Harbor, New York, 2000.
[20]
A. V. Oppenheim and R. W. Schafer. Digital Signal Processing. Englewood Cliffs, N. J., 1975.
[21]
K. Otsuka, T. Horikoshi, S. Suzuki, and H. Kojima. Memory-based forecasting for weather image patterns. In Proceedings of the 17th Conference on Artificial Intelligence (AAAI), pages 330--336, July 2000.
[22]
L. Rabiner and B.-H. Juang. Fundamentals of Speech Recognition. Englewood Cliffs, N. J., 1993.
[23]
R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In Proceedings of VLDB, pages 194--205, August 1998.
[24]
M. V. Wickerhauser. Adapted Wavelet Analysis from Theory to Software. A K Peters Ltd, Massachusetts, 1994.
[25]
B.-K. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In Proceedings of VLDB, pages 385--394, September 2000.
[26]
B.-K. Yi, H. V. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time warping. In Proceedings of ICDE, pages 201--208, February 1998.
[27]
Y. Zhu and D. Shasha. Warping indexes with envelope transforms for query by humming. In Proceedings of ACM SIGMOD, pages 181--192, June 2003.

Cited By

View all
  • (2025)IntroductionFault Diagnosis and Prognostics Based on Cognitive Computing and Geometric Space Transformation10.1007/978-981-99-8917-1_1(1-43)Online publication date: 3-Jan-2025
  • (2024)High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision SensorsProceedings of the ACM on Management of Data10.1145/36549462:3(1-27)Online publication date: 30-May-2024
  • (2024)MGINS: A Lane-Level Localization System for Challenging Urban Environments Using Magnetic Field Matching/GNSS/INS FusionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338656825:10(14890-14904)Online publication date: Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
PODS '05: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
June 2005
388 pages
ISBN:1595930620
DOI:10.1145/1065167
  • General Chair:
  • Georg Gottlob,
  • Program Chair:
  • Foto Afrati
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2005

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Article

Conference

SIGMOD/PODS05

Acceptance Rates

Overall Acceptance Rate 642 of 2,707 submissions, 24%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)34
  • Downloads (Last 6 weeks)6
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)IntroductionFault Diagnosis and Prognostics Based on Cognitive Computing and Geometric Space Transformation10.1007/978-981-99-8917-1_1(1-43)Online publication date: 3-Jan-2025
  • (2024)High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision SensorsProceedings of the ACM on Management of Data10.1145/36549462:3(1-27)Online publication date: 30-May-2024
  • (2024)MGINS: A Lane-Level Localization System for Challenging Urban Environments Using Magnetic Field Matching/GNSS/INS FusionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338656825:10(14890-14904)Online publication date: Oct-2024
  • (2024)Accelerating time series similarity search under Move-Split-Merge distance via dissimilarity space embeddingExpert Systems with Applications10.1016/j.eswa.2024.124889255(124889)Online publication date: Dec-2024
  • (2024)Automated regression test method for scientific computing libraries: Illustration with SPHinXsysJournal of Hydrodynamics10.1007/s42241-024-0042-636:3(466-478)Online publication date: 26-Jul-2024
  • (2024)Transfer-learning-based representation learning for trajectory similarity searchGeoInformatica10.1007/s10707-024-00515-x28:4(631-648)Online publication date: 13-Apr-2024
  • (2023)pSafety: Privacy-Preserving Safety Monitoring in Online Ride Hailing ServicesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2021.313057120:1(209-224)Online publication date: 1-Jan-2023
  • (2023)On computing exact means of time series using the move-split-merge metricData Mining and Knowledge Discovery10.1007/s10618-022-00908-237:2(595-626)Online publication date: 9-Jan-2023
  • (2023)Efficient Similarity Searches for Multivariate Time Series: A Hash-Based ApproachInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_43(478-490)Online publication date: 22-Nov-2023
  • (2022)A Position Fixing Method for Near-Bottom Camera Data on the SeafloorMinerals10.3390/min1208103412:8(1034)Online publication date: 17-Aug-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media