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Source Exploitation

  • Witold Abramowicz
  • Paweł Kalczyński
  • Krzysztof Węcel

Abstract

In this chapter we shall present example information sources on the Web and the innovative techniques for exploiting business portals: Information Ants and Indexing Parsers. The former aims at efficient spotting of new or updated content in large hypertext-based collections of documents on the Web. This is particularly useful for mechanical filtering, as filters process only new content. The latter technique enables taking advantage of structured elements of contemporary markup-language-based Web documents. This concept also includes processing source-specific tags, defined by content providers.

Keywords

Data Warehouse Content Provider Source Exploitation Filter Information Internet Source 
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.

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References

  1. Abramowicz W, Kalczytíski PJ, Wgcel K (2001e) Information Ants to Filter Business Information from Internet Sources. Proc of CAINE 2001, Las Vegas, USA, pp 134-137Google Scholar
  2. ACO 2001, Ant Colony Optimization home page, iridia.ulb.ac.be/—mdorigo/ACO/ACO.htmlGoogle Scholar
  3. Aho A, Corasick M (1975) Efficient String Matching: An Aid to Bibliographic Search. Communications of the ACM, Vol. 18(6)Google Scholar
  4. Allcock S, Plenty A, Webber S, Yeates R (1999) Business Information and the Internet: Use of the Internet as an Information Resource for Small and Medium-sized Enterprises: Final Report. British Library Research and Innovation Report, RIC/G/381. London, EnglandGoogle Scholar
  5. Baeza-Yates R, Ribeiro-Neto B (1999) Modern Information Retrieval. Addison-Wesley ACM Press New York, USAGoogle Scholar
  6. Bonabeau E, Dorigo M, Theraulaz G (2000) Inspiration for Optimization from Social Insect Behavior. Nature, Vol. 406, 2000, pp 39-42CrossRefGoogle Scholar
  7. Dorigo M, Di Caro G (1999) The Ant Colony Optimization Meta-Heuristic. In: Come D,Google Scholar
  8. Dorigo M, Glover F (eds) New Ideas in Optimization. McGraw-Hill, pp 11-32Google Scholar
  9. Dumais ST, Furnas GW, Landauer TK, Deerwester S (1988) Using Latent Semantic Analysis to Improve Information Retrieval. Proceedings of ACM CHI'88 Conference on Human Factors in Computing, New York, pp 281-285Google Scholar
  10. Hackathorn R (1999) Web Farming for the Data Warehouse. Morgan Kaufman Publishers, San Francisco, USAGoogle Scholar
  11. Kalczyíiski PJ (2000) HyperSDI zasilaJ4cy hurtownig danych informacjami benchmarkingowymi (HyperSDI Supplying the Data Warehouse with Benchmarking Information), Master Thesis, Department of Computer Science, Faculty of Economics, The Poznatí University of Economics, Poznarí, Poland (in Polish)Google Scholar
  12. Porter M (1980) An Algorithm for Suffix Stripping. Program 14(3), pp 130-137CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Witold Abramowicz
    • 1
  • Paweł Kalczyński
    • 1
  • Krzysztof Węcel
    • 1
  1. 1.Department of Computer ScienceThe Poznań University of EconomicsPoznańPoland

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