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Data Warehouses: Next Challenges

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Business Intelligence (eBISS 2011)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 96))

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Summary

Data Warehouses are a fundamental component of today’s Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated resaerch in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP.

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Author information

Authors and Affiliations

  1. Department of Computer and Decision Engineering (CoDE), Université Libre de Bruxelles, Belgium

    Alejandro Vaisman & Esteban Zimányi

Authors
  1. Alejandro Vaisman
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  2. Esteban Zimányi
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Editors and Affiliations

  1. MAS Laboratory, Ecole Centrale Paris, 92 295, Châtenay-Malabry Cedex, France

    Marie-Aude Aufaure

  2. Department of Computer & Decision Engineering (CoDE) CP 165/15, Universite Libre de Bruxelles, 1050, Brussels, Belgium

    Esteban Zimányi

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Vaisman, A., Zimányi, E. (2012). Data Warehouses: Next Challenges. In: Aufaure, MA., Zimányi, E. (eds) Business Intelligence. eBISS 2011. Lecture Notes in Business Information Processing, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27358-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-27358-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-642-27358-2

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