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Managing Modular Ontology Evolution Under Big Data Integration

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Information Systems (EMCIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 299))

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Abstract

Big Data integration frameworks provide unified view of the data available from heterogeneous data sources. These data sources are continuously evolving, forcing systems that integrate them to adapt their global schema after each change. This gets more challenging when aiming to maintain the global schema always reflecting data sources content. To cope with such complexity, in this paper we describe evolution scenarios and manage modular ontology evolution within Big Data integration framework in an a priori way according to changes performed against the data sources.

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Notes

  1. 1.

    https://www.w3.org/TR/owl-features/.

  2. 2.

    http://www.mongodb.org/.

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Author information

Authors and Affiliations

  1. MIRACL Laboratory, Higher Institute of Computer Science and Multimedia, Sfax University, Sfax, Tunisia

    Hanen Abbes & Faiez Gargouri

Authors
  1. Hanen Abbes
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  2. Faiez Gargouri
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Corresponding author

Correspondence to Hanen Abbes .

Editor information

Editors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Marinos Themistocleous

  2. Department of Management and Technology, Bocconi University, Milan, Italy

    Vincenzo Morabito

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Abbes, H., Gargouri, F. (2017). Managing Modular Ontology Evolution Under Big Data Integration. In: Themistocleous, M., Morabito, V. (eds) Information Systems. EMCIS 2017. Lecture Notes in Business Information Processing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-65930-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-65930-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65929-9

  • Online ISBN: 978-3-319-65930-5

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