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A Hierarchical Representation for Recording Semantically Condensed Data from Physically Massive Data Out of Sensor Networks Geographically Dispersed

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On the Move to Meaningful Internet Systems: OTM 2009 Workshops (OTM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5872))

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Abstract

A number of sensor networks may produce a huge amount of data, and there has been a necessity the data are processed in a single system. However the data could early overwhelm the database of the system. This work introduces a condensing method to reduce the amount of data exploiting its semantics. The condensing reduces the amount of data to be transmitted and stored, by condensing the data according to semantics shared among servers. The briefed data could diminish the load of applications running on resource-constrained devices in pervasive computing.

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Author information

Authors and Affiliations

  1. Korea Railroad Research Institute, Woulam, Uiwang, Gyeonggi, Korea

    MinHwan Ok

Authors
  1. MinHwan Ok
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Editor information

Editors and Affiliations

  1. STARLab, Vrije Universiteit Brussel (VUB), Bldg G/10, Pleinlaan 2, 1050, Brussels, Belgium

    Robert Meersman

  2. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660, Boadilla del Monte, Madrid, Spain

    Pilar Herrero

  3. DEBII - CBS, De Laeter Way, Curtin University of Technology, 6102, Bentley, WA, Australia

    Tharam Dillon

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© 2009 Springer-Verlag Berlin Heidelberg

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Ok, M. (2009). A Hierarchical Representation for Recording Semantically Condensed Data from Physically Massive Data Out of Sensor Networks Geographically Dispersed. In: Meersman, R., Herrero, P., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2009 Workshops. OTM 2009. Lecture Notes in Computer Science, vol 5872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05290-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-05290-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05289-7

  • Online ISBN: 978-3-642-05290-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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