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Approximate String Matching for Multiple-Attribute, Large-Scale Customer Address Databases

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Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access (ICADL 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2911))

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Abstract

The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale customer address information for the Credit Collection industry. The proposed solution relies on the edit distance error model and the q-gram string filtering technique. We present an algorithm that integrates the methodology with existing RDBMS through SQL-based stored procedures.

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References

  1. Gravano, L., Ipeirotis, P.G., Jagadish, H.V., Koudas, N., Muthukrishnan, S., Pietarinen, L., Srivastava, D.: Using q-grams in a DBMS for Approximate String Processing. IEEE Data Engineering Bulletin 24(4), 28–34 (2001)

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Author information

Authors and Affiliations

  1. Intelligent Systems and Robotics Lab, Nanyang Technological University, Singapore, 639798

    Y. M. Cheong & J. C. Tay

Authors
  1. Y. M. Cheong
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  2. J. C. Tay
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Editor information

Editors and Affiliations

  1. Department of Information Science, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, UKM Selangor, Malaysia

    Tengku Mohd Tengku Sembok

  2. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Halimah Badioze Zaman

  3. Department of Management Information Systems, Eller College of Management, The University of Arizona, AZ 85721, USA

    Hsinchun Chen

  4. International School of Information Management, University of Mysore, Mysore, India

    Shalini R. Urs

  5. School of Engineering, Information and Communications University, 119, Munjiro, Yuseong-gu, 305-732, Daejeon, Korea

    Sung-Hyon Myaeng

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© 2003 Springer-Verlag Berlin Heidelberg

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Cheong, Y.M., Tay, J.C. (2003). Approximate String Matching for Multiple-Attribute, Large-Scale Customer Address Databases. In: Sembok, T.M.T., Zaman, H.B., Chen, H., Urs, S.R., Myaeng, SH. (eds) Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access. ICADL 2003. Lecture Notes in Computer Science, vol 2911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24594-0_16

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  • DOI: https://doi.org/10.1007/978-3-540-24594-0_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20608-8

  • Online ISBN: 978-3-540-24594-0

  • eBook Packages: Springer Book Archive

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