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Mining Schemas in Semistructured Data Using Fuzzy Decision Trees

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3495))

Abstract

As WWW has become a huge information resource, it is very important for us to utilize this kind of information effectively. However, the information on WWW can’t be queried and manipulated in a general way.

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References

  1. Goldman, R., Widom, J.: Dataguides: Enabling query formulation and optimization in semistructured databases. In: Proceedings of the 23rd International Conference on Very Large Data Bases (1997)

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  2. Goldman, R., Widom, J.: Approximate dataguides. Technical report, Stanford University (1998)

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  3. Nestorov, S., Abiteboul, S., Motwani, R.: Inferring structure in semistructured data. In: Proceedings of the Workshop on Management of Semistructured Data (1997)

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  4. Nestorov, S., Abiteboul, S., Motwani, R.: Extracting schema from semistructured data. In: Proceedings of ACM SIGMOD International Conference on Management of Data (1998)

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© 2005 Springer-Verlag Berlin Heidelberg

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Sun, W., Liu, Dx. (2005). Mining Schemas in Semistructured Data Using Fuzzy Decision Trees. In: Kantor, P., et al. Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427995_70

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  • DOI: https://doi.org/10.1007/11427995_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25999-2

  • Online ISBN: 978-3-540-32063-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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