Skip to main content

Discovering Temporal Patterns for Interval-based Events

  • Conference paper
  • First Online:
Data Warehousing and Knowledge Discovery (DaWaK 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1874))

Included in the following conference series:

Abstract

In many daily transactions, the time when an event takes place is known and stored in databases. Examples range from sales records, stock exchange, patient records, to scientific databases in geophysics and astronomy. Such databases incorporate the concept of time which describes when an event starts and ends as historical records [9]. The temporal nature of data provides us with a better understanding of the trend or pattern over time. In market-basket data, we can have a pattern like “75% of customers buy peanuts when butter starts to be in big sales and before bread is sold out”. We observe that there may be some correlations among peanuts, butter and bread so that we can have better planning for marketing strategy. Knowledge discovery in temporal databases thus catches the attention of researchers [8, 4].

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

Access this chapter

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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping 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

Refrences

  1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, pages 487–499, 1994.

    Google Scholar 

  2. R. Agrawal and R. Srikant. Mining sequential patterns. In 11th International Conf. on Data Engineering, pages 3–14, March 1995.

    Google Scholar 

  3. J. F. Allen. Maintaining knowledge about temporal intervals. In Communications of the ACM 26(11), pages 832–843, 1983.

    Article  MATH  Google Scholar 

  4. X. Chen and I. Petrounias. An architecture for temporal data mining. In IEE Colloqium on Knowledge Discovery and Data Mining, pages 8-1–8-4, 1998.

    Google Scholar 

  5. H. Mannila and H. Toivonen. Discovering generalized episodes using minimal occurrences. In 2nd International Conf. on Knowledge Discovery and Data Mining, pages 146–151, August 1996.

    Google Scholar 

  6. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episodes in sequences. In 1st International Conf. on Knowledge Discovery and Data Mining, pages 210–215, August 1995.

    Google Scholar 

  7. B. Padmanabhan and A. Tuzhilin. Pattern discovery in temporal databases: A temporal logic approach. In 2nd International Conf. on Knowledge Discovery and Data Mining, pages 351–354, August 1996.

    Google Scholar 

  8. M. H. Saraee and B. Theodoulidis. Knowledge discovery in temporal databases. In IEE Colloquium on Digest No. 1995/021(A), pages 1-1–1-4, 1995.

    Google Scholar 

  9. A. Tansel, J. Clifford, S. Gadia, S. Jajodia, A. Segev, and R. Snodgrass. Temporal Databases: Theory, Design, and Implementation. Benjamin/Cummings, 1993.

    Google Scholar 

  10. M. J. Zaki. Fast mining of sequential patterns in very large databases. Technical report, Technical Report 668 of the Department of Computer Science, University of Rochester, Nov 1997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kam, Ps., Fu, A.Wc. (2000). Discovering Temporal Patterns for Interval-based Events. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2000. Lecture Notes in Computer Science, vol 1874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44466-1_32

Download citation

  • DOI: https://doi.org/10.1007/3-540-44466-1_32

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67980-6

  • Online ISBN: 978-3-540-44466-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics