Skip to main content

Experiments with Document Archive Size Detection

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2633))

Abstract

The size of a document archive is a very important parameter for resource selection in distributed information retrieval systems. In this paper, we present a method for automatically detecting the size (i.e. number of documents) of a document archive, in case the archive itself does not provided such information. In addition, a method for detecting the incremental change of the archive size is also presented, which can be useful for deciding if a resource description has become obsolete and needs to be regenerated. An experimental evaluation of these methods shows that they provide quite accurate information.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. K. Bharat and A. Z. Broder. A Technique for Measuring the Relative Size and Overlap of Public Web Search Engines. In Proceedings of 7th WWW Conference, Brisbane, Australia, April 1998.

    Google Scholar 

  2. J. K. Callan, M. Connell, and A. Du. Automatic Discovery of Language Models for Text Databases. In Proceedings of ACM SIGMOD International Conference, pages 479–490, Philadelphia, USA, May 1999.

    Google Scholar 

  3. J. K. Callan, Z. Lu, and W. Croft. Searching Distributed Collections with Inference Networks. In Proceedings of the 18th Annual International ACM SIGIR Conference, pages 21–28, Seattle, USA, June 1995.

    Google Scholar 

  4. Norbert Fuhr. A Decision-Theoretic Approach to Database Selection in Networked IR. ACM Transaction on Information Systems, 17(3):229–249, 1999.

    Article  Google Scholar 

  5. L. Gravano and H. García-Molina. Generalizing GlOSS to Vector-Space Database and Broker Hierarchies. In Proceedings of 21st VLDB International Conference, pages 78–89, Zürich, Switzerland, 1995.

    Google Scholar 

  6. S. Lawrence and C. L. Giles. Searching the World Wide Web. Science, 280(3):98–100, April 1998.

    Article  Google Scholar 

  7. K. Liu, C. Yu, and W. Meng. Discovering the Representative of a Search Engine. In Proceedings of the ACM CIKM International Conference, pages 652–654, Mclean, Virginia, USA, November 2002.

    Google Scholar 

  8. P. Ogilvie and J. Callan. Experiments using the Lemur Toolkit. In Proceedings of the 2001 Text Retrieval Conference (TREC), pages 103–108, Gaithersburg, Maryland, USA, November 2001.

    Google Scholar 

  9. S. Wu and F. Crestani. Multi-objective Resource Selection in Distributed Information Retrieval. In Proceedings of IPMU02, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 1171–1178, Annecy, France, July 2002.

    Google Scholar 

  10. B. Yuwono and D. Lee. Server Ranking for Distributed Test Retrieval Systems on the Internet. In Proceedings of the Fifth International Conference on Database Systems for Advanced Application, pages 41–50, Melbourne, Australia, April 1997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, S., Gibb, F., Crestani, F. (2003). Experiments with Document Archive Size Detection. In: Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2003. Lecture Notes in Computer Science, vol 2633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36618-0_21

Download citation

  • DOI: https://doi.org/10.1007/3-540-36618-0_21

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01274-0

  • Online ISBN: 978-3-540-36618-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics