Sequence Data Indexing Method based on Minimum DTW Distance

최소 DTW 거리 기반의 데이터 시퀀스 색인 기법

  • Received : 2011.11.28
  • Accepted : 2011.12.14
  • Published : 2011.12.28


In this paper, we propose an indexing method to support efficient similarity search for sequence databases. We present a new distance measurement called minimum DTW distance to enhance the filtering effects. The minimum DTW distance is to measure the minimum distance between a sequence data and the group of similar sequences. It enables similarity search through hierarchical index structure by filtering sequence databases. Finally, we show the superiority of our method through some experiments.


Time Series;DTW;Index;Sequence


  1. I. Assent, M. Wichterich, R. Krieger, H. Kremer, and T. Seidl, "Anticipatory DTW for Efficieint Similarity Search in Time Series Databases," Proceedings of the VLDB Endowment, pp.826-837, 2009.
  2. E. Keogh and C.A. Ratanamahatana, "Exact Indexing of Dynamic Time Warping," Knowledge and Information Systems, Vol.7, No.3, pp.358-386, 2005.
  3. 김상욱, 박상현, "시퀀스 데이터베이스에서 타임 워핑을 지원하는 효과적인 유사 검색 기법", 정보 과학회논문지:데이터베이스, 제28권, 제4호, pp.643-654, 2001.
  4. 한욱신, 이진수, 문양세, "DTW 거리를 지원하는 범위 서브시퀀스 매칭", 정보과학회논문지:컴퓨팅의 실제 및 레터, 제14권, 제6호, pp.559-566, 2008.
  5. Y. Zhu and D. Shasha, "Warping Indexes with Envelope Transforms for Query by Humming," Proceedings of the ACM SIGMOD, pp.181-192, 2003.
  6. S. Salvador and P. Chan, "FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Spaces," Proceedings of KDD Workshop on Mining Temporal and Sequential Data, pp.70-80, 2004.
  7. Y. Sakurai, M. Yoshikawa, and C. Faloutsos, "FTW : Fast Similiarity Search under the Time Warping Distance," Proceedings of ACM PODS, pp.326-337, 2005.
  8. V. Athitsos, P. Papapetrou, M. Potamias, G. Kollios, and D. Gunopulos, "Approximate Embedding-based Subsequence Matching of Time Series," Proceedings of ACM SIGMOD, pp.365-378, 2008.
  9. A. Guttman, "R-trees: A Dynamic Index Structure for Spatial Searching," Proceedings of ACM SIGMOD, pp.47-57, 1984.
  10. T. Warrenliao, "Clustering of Time Series Data -a Survey," Pattern Recognition, Vol.38, No.11, pp.1857-1874, 2005.
  11. R. Weber, H. J. Schek, and S. Blott, "A Quantitative Analysis and Performance Study for Similarity Search Methods in Highdimensional Spaces," Proceedings of VLDB, pp.194-205, 1998.
  12. 복경수, 허정필, 유재수, "동적 비트 할당을 통한 다차원 벡터 근사 트리", 한국콘텐츠학회논문지, 제4권, 제3호, pp.81-90, 2004.