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Fast Methods with Magic Sampling for Knowledge Discovery in Deductive Databases with Large Deduction Results

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Book cover Advances in Database Technologies (ER 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1552))

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Abstract

The ability of the deductive database to handle recursive queries is one of its most useful features. It opens up new possibilities for users to view and analyze data. This ability to handle recursive queries, however, still cannot be fully utilized because when recursive rules are involved, the amount of deduced facts can become very large, making it difficult and sometimes impossible to store, view or analyze the query results. In order to overcome this problem, we have proposed the DSK method and the DSK(S) method to discover characteristic rules from large amount of deduction results without having to store all of them. In this paper, we propose two new methods, the DSK(T) method and the DSK(ST) method which are faster than the DSK method and the DSK(S) method respectively. In addition, we propose a new sampling method called magic sampling, which is used by the two methods to achieve the improvement in speed. Magic sampling works when linear recursive rules are involved and the magic set algorithm is used for deduction.

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Author information

Authors and Affiliations

  1. Department of Information Systems Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565, Japan

    Chien-Le Goh, Masahiko Tsukamoto & Shojiro Nishio

Authors
  1. Chien-Le Goh
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  2. Masahiko Tsukamoto
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  3. Shojiro Nishio
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Editor information

Editors and Affiliations

  1. Graduate School of Informatics, Dept. of Social Informatics, Kyoto University, 606-8501, Yoshida Sakyo Kyoto, Japan

    Yahiko Kambayashi

  2. Dept. of Computer Science Clear Water Bay, Hong Kong University of Science and Technology, Hong Kong, China

    Dik Lun Lee

  3. School of Applied Science, Nanyang Technological University, N4-2A-12, Nanyang Avenue, 639798, Singapore

    Ee-Peng Lim

  4. School of Computer and Information Science The Levels Campus, University of South Australia, Mawson Lakes, 5095, S.A., Australia

    Mukesh Kumar Mohania

  5. Faculty of Science, Dept. of Information Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, 112-8610, Tokyo, Japan

    Yoshifumi Masunaga

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© 1999 Springer-Verlag Berlin Heidelberg

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Goh, CL., Tsukamoto, M., Nishio, S. (1999). Fast Methods with Magic Sampling for Knowledge Discovery in Deductive Databases with Large Deduction Results. In: Kambayashi, Y., Lee, D.L., Lim, EP., Mohania, M.K., Masunaga, Y. (eds) Advances in Database Technologies. ER 1998. Lecture Notes in Computer Science, vol 1552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49121-7_2

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  • DOI: https://doi.org/10.1007/978-3-540-49121-7_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65690-6

  • Online ISBN: 978-3-540-49121-7

  • eBook Packages: Springer Book Archive

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