Using Knowledge Base for Event-Driven Scheduling of Web Monitoring Systems

  • Yang Sok Kim
  • Sung Won Kang
  • Byeong Ho Kang
  • Paul Compton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5692)


Web monitoring systems report any changes to their target web pages by revisiting them frequently. As they operate under significant resource constraints, it is essential to minimize revisits while ensuring minimal delay and maximum coverage. Various statistical scheduling methods have been proposed to resolve this problem; however, they are static and cannot easily cope with events in the real world. This paper proposes a new scheduling method that manages unpredictable events. An MCRDR (Multiple Classification Ripple-Down Rules) document classification knowledge base was reused to detect events and to initiate a prompt web monitoring process independent of a static monitoring schedule. Our experiment demonstrates that the approach improves monitoring efficiency significantly.


web monitoring scheduling MCRDR 


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  1. 1.
    Liu, L., Pu, C., Han, W.: CONQUER: a continual query system for update monitoring in the WWW. Computer Systems Science and Engineering 14(2), 99–112 (1999)Google Scholar
  2. 2.
    Naughton, J., et al.: The Niagara internet query system. IEEE Data Engineering Bulletin 24(2), 27–33 (2001)Google Scholar
  3. 3.
    Liu, L., Pu, C., Tang, W.: Continual Queries for Internet Scale Event-Driven Information Delivery. IEEE Transactions on Knowledge and Data Engineering 11(4), 610–628 (1999)CrossRefGoogle Scholar
  4. 4.
    Liu, L., Pu, C., Tang, W.: WebCQ: Detecting and delivering information changes on the Web. In: CIKM 2000. ACM Press, Washington D.C (2000)Google Scholar
  5. 5.
    Pandey, S., Ramamritham, K., Chakrabarti, S.: Monitoring the dynamic web to respond to continuous queries. In: WWW 2003, Budapest, Hungary (2003)Google Scholar
  6. 6.
    Pandey, S., Dhamdhere, K., Olston, C.: WIC: A General-Purpose Algorithm for Monitoring Web Information Sources. In: 30th VLDB Conference, Toronto, Canada (2004)Google Scholar
  7. 7.
    Bright, L., Gal, A., Raschid, L.: Adaptive pull-based policies for wide area data delivery. ACM Transactions on Database Systems (TODS) 31(2), 631–671 (2006)CrossRefGoogle Scholar
  8. 8.
    Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, University of Calgary (1995)Google Scholar
  9. 9.
    Kim, Y.S., et al.: Adaptive Web Document Classification with MCRDR. In: International Conference on Information Technology: Coding and Computing ITCC 2004, Orleans, Las Vegas, Nevada, USA (2004)Google Scholar
  10. 10.
    Park, S.S., Kim, Y.S., Kang, B.H.: Web Document Classification: Managing Context Change. In: IADIS International Conference WWW/Internet 2004, Madrid, Spain (2004)Google Scholar
  11. 11.
    Kim, Y.S., et al.: Incremental Knowledge Management of Web Community Groups on Web Portals. In: 5th International Conference on Practical Aspects of Knowledge Management, Vienna, Austria (2004)Google Scholar
  12. 12.
    Kim, Y.S., et al.: Knowledge Acquisition Behavior Anaysis in the Open-ended Document Classification. In: 19th ACS Australian Joint Conference on Artificial Intelligence, Hobart, Australia (2006)Google Scholar
  13. 13.
    Kang, B.-h., Kim, Y.S., Choi, Y.J.: Does multi-user document classification really help knowledge management? In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS, vol. 4830, pp. 327–336. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Brewington, B.E., Cybenko, G.: Keeping Up with the Changing Web. Computer 33(5), 52–58 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yang Sok Kim
    • 1
    • 2
  • Sung Won Kang
    • 2
  • Byeong Ho Kang
    • 2
  • Paul Compton
    • 1
  1. 1.School of Computer Science and EngineeringThe University of New South WalesSydneyAustralia
  2. 2.School of Computing and Information SystemsUniversity of TasmaniaHobartAustralia

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