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Information Retrieval of Sequential Data in Heterogeneous XML Databases

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Adaptive Multimedia Retrieval: User, Context, and Feedback (AMR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3877))

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Abstract

The XML language is a W3C standard sustained by both the industry and the scientific community. Therefore, the available information annotated in XML keeps and will keep increasing in size. Furthermore, not only the volume of the XML information is increasing but also its complexity. The XML documents evolved from plain structured text representations, to documents having complex and heterogeneous structures and contents like sequential or time series data. In this article we introduce a retrieval scheme designed to manage sequential data in an XML context based on two levels of approximation: on the structural localization/organization of the sequential data and on its content. To this end we merge methods developed in two different research areas: XML information retrieval and sequence similarity search.

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Author information

Authors and Affiliations

  1. Valoria Laboratory, University of South-Brittany, BP 573, 56017 Cedex, Vannes, France

    Eugen Popovici, Pierre-François Marteau & Gildas Ménier

Authors
  1. Eugen Popovici
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  2. Pierre-François Marteau
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  3. Gildas Ménier
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Editor information

Editors and Affiliations

  1. Laboratoire d’Informatique de Paris 6, France

    Marcin Detyniecki

  2. Department of Computer Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, UK

    Joemon M. Jose

  3. Fakultät für Informatik, Otto-von-Guericke Universität Madgeburg, Universitätsplatz 2, 39106, Germany

    Andreas Nürnberger

  4. Department of Computing Science, University of Glasgow, G12 8QQ, Glasgow, UK

    C. J. van Rijsbergen

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© 2006 Springer-Verlag Berlin Heidelberg

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Popovici, E., Marteau, PF., Ménier, G. (2006). Information Retrieval of Sequential Data in Heterogeneous XML Databases. In: Detyniecki, M., Jose, J.M., Nürnberger, A., van Rijsbergen, C.J. (eds) Adaptive Multimedia Retrieval: User, Context, and Feedback. AMR 2005. Lecture Notes in Computer Science, vol 3877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11670834_19

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  • DOI: https://doi.org/10.1007/11670834_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32174-3

  • Online ISBN: 978-3-540-32175-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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