SpringerLink
Log in

Micro Similarity Queries in Time Series Database

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

Included in the following conference series:

  • 1316 Accesses

Abstract

Currently there is no model available that would facilitate the task of finding similar time series based on partial information that interest users. We studied a novel query problem class that we termed micro similarity queries (MSQ) in this paper. We present the formal definition of MSQ. A method is investigated for the purpose of efficient processing of MSQ. We evaluated the behavior of MSQ problem and our query algorithm with both synthetic data and real data. The results show that the knowledge revealed by MSQ corresponds with the subjective feeling of similarity based on singular interest.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Rakesh A, Christos F, Efficient Similarity Search in Sequence Databases, FODO 1993, pages 69–84.

    Google Scholar 

  2. C. Faloutsos, M. Ranganathan, Y. Manolopoulos, Fast Subsequence Matching in Time-Series Database, Proc. of ACM SIGMOD 1994, Page 419–429.

    Google Scholar 

  3. Gautam D, King-IP L, Heikki M, Gopal R, Padhraic S. Rule Discovery from Time Series. Proc. of the 4th Intl. Conf. on KDD, 1998, pages 16–22.

    Google Scholar 

  4. Hagit S, Stanley B.Z. Approximate Queries and Representations for Large Data Sequences. Proc. of the 12th Intl. Conf. on data engineering, 1996, pages 536–545.

    Google Scholar 

  5. R. Agrawal, K.I. Lin, H.S. Sawhney, K. Shim, Fast Similarity Search in the Presence of Noise, Scaling and Translation in Time-Series Databases, The 23rd Intl. Conf. on Very Large Data Bases, 1995, pages 490–501.

    Google Scholar 

  6. D. Hull. Improving Text Retrieval for the Routing Problem Using Latent Semantic Indexing. In Proc. Of the 17th ACM-SIGIR Conference, 1994, pages 282–291.

    Google Scholar 

  7. N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger. The R* tree: An Efficient and Robust Access Method for Points and Rectabgles. Proc. Of ACM SIGMOD 1990, pages 322–331

    Google Scholar 

  8. D. White and R. Jain. Similarity Indexing with the SS-tree. Proc. Of 12th Int. Conf. On Data Engineering, 1996, pages 516–523.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The State Key Laboratory of Intelligent Technology and System Computer Science and Technology Dept., TsingHua University, Bei**g, China, 100084

    **ao-ming **, Yuchang Lu & Chunyi Shi

Authors
  1. **ao-ming **
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuchang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunyi Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

**, Xm., Lu, Y., Shi, C. (2001). Micro Similarity Queries in Time Series Database. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_38

Download citation

  • DOI: https://doi.org/10.1007/3-540-45357-1_38

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation