Advertisement

On similarity queries for time-series data: Constraint specification and implementation

  • Dina Q. Goldin
  • Paris C. Kanellakis
Databases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 976)

Abstract

Constraints are a natural mechanism for the specification of similarity queries on time-series data. However, to realize the expressive power of constraint programming in this context, one must provide the matching implementation technology for efficient indexing of very large data sets. In this paper, we formalize the intuitive notions of exact and approximate similarity between time-series patterns and data. Our definition of similarity extends the distance metric used in [2, 7] with invariance under a group of transformations. Our main observation is that the resulting, more expressive, set of constraint queries can be supported by a new indexing technique, which preserves all the desirable properties of the indexing scheme proposed in [2, 7].

Keywords

False Alarm Normal Form Internal Representation Similarity Distance Indexing Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. Aho. Algorithms for Finding Patterns in Strings. Handbook of TCS., J. Van Leeuwen editor, volume A, chapter 5, Elsevier, 1990.Google Scholar
  2. 2.
    R. Agrawal, C. Faloutsos, A. Swami. Efficient Similarity Search in Sequence Databases. FODO Conf., Evanston, Ill., Oct. 1993Google Scholar
  3. 3.
    M. Baudinet, M. Niezette, P. Wolper. On the Representation of Infinite Temporal Data and Queries. Proc. 10th ACM PODS, 280–290, 1991.Google Scholar
  4. 4.
    A. Brodsky, J. Jaffar, M.J. Maher. Toward Practical Constraint Databases. Proc. 19th VLDB, 322–331, 1993.Google Scholar
  5. 5.
    A. Colmerauer. An Introduction to Prolog III. CACM, 33:7:69–90, 1990.Google Scholar
  6. 6.
    M. Dincbas, P. Van Hentenryck, H. Simonis, A. Aggoun, T. Graf, F. Berthier. The Constraint Logic Programming Language CHIP. Proc. Fifth Generation Computer Systems, Tokyo Japan, 1988.Google Scholar
  7. 7.
    C. Faloutsos, M. Ranganathan, Y. Manolopoulos. Fast Subsequence Matching in Time-Series Databases. Proc. ACM SIGMOD Conf., pp. 419–429, May 1994Google Scholar
  8. 8.
    H.V. Jagadish. A Retrieval Technique for Similar Shapes. Proc. ACM SIGMOD Conf., pp. 208–217, May 1991Google Scholar
  9. 9.
    H. V. Jagadish, A. O. Mendelzon, T. Milo. Similarity-Based Queries. to appear in Proc. 14th ACM PODS, 1995Google Scholar
  10. 10.
    J. Jaffar, J.L. Lassez. Constraint Logic Programming. Proc. 14th ACM POPL, 111–119, 1987.Google Scholar
  11. 11.
    F. Kabanza, J-M. Stevenne, P. Wolper. Handling Infinite Temporal Data. Proc. 9th ACM PODS, 392–403, 1990.Google Scholar
  12. 12.
    P. C. Kanellakis, G. M. Kuper, P. Z. Revesz. Constraint Query Languages. Proc. 9th ACM PODS, 299–313, 1990. Full version available as Brown Univ. Tech. Rep. CS-92-50. To appear in JCSS.Google Scholar
  13. 13.
    P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, J. S. Vitter. Indexing for Data Models with Constraints and Classes. Proc. 12th ACM PODS, 233–243, 1993.Google Scholar
  14. 14.
    R. M. Karp and M. O. Rabin. Efficient Randomized Pattern-Matching Algorithms. IBM J. Res. Develop., 31(2), 1987Google Scholar
  15. 15.
    Modenov and Pakhomenko. Geometric Transformations, Academic Press, 1965Google Scholar
  16. 16.
    A.V. Oppenheim and R.W. Schafer. Digital Signal Processing, Prentice Hall, 1975Google Scholar
  17. 17.
    H. Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley, Reading MA, 1990.Google Scholar
  18. 18.
    P. Sheshadri, M. Livny, R. Ramakrishnan. Sequence Query Processing Proc. ACM SIGMOD Conf., pp. 430–441, May 1994Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Dina Q. Goldin
    • 1
  • Paris C. Kanellakis
    • 1
  1. 1.Department of Computer ScienceBrown UniversityProvidence RIUSA

Personalised recommendations