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Making Data Meaningful: The Business Intelligence Model and Its Formal Semantics in Description Logics

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Book cover On the Move to Meaningful Internet Systems: OTM 2012 (OTM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7566))

Abstract

Business Intelligence (BI) offers great opportunities for strategic analysis of current and future business operations; however, existing BI tools typically provide data-oriented responses to queries, difficult to understand in terms of business objectives and strategies. To make BI data meaningful, we need a conceptual modeling language whose primitive concepts represent business objectives, processes, opportunities and threats. We have previously introduced such a language, the Business Intelligence Model (BIM). In this paper we consolidate and rationalize earlier work on BIM, giving a precise syntax, reducing the number of fundamental concepts by using meta-attributes, and introducing the novel notion of “pursuit”. Significantly, we also provide a formal semantics of BIM using a subset of the OWL Description Logic (DL). Using this semantics as a translation, DL reasoners can be exploited to (1) propagate evidence and goal pursuit in support of “what if?” reasoning, (2) allow extensions to the BIM language, (3) detect inconsistencies in specific BIM models, and (4) automatically classify defined concepts relative to existing concepts, organizing the model.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Toronto, Canada

    Jennifer Horkoff, John Mylopoulos, Daniele Barone & Lei Jiang

  2. Dept. of Computer Science, Rutgers University, USA

    Alex Borgida

  3. Faculty of Information, University of Toronto, Canada

    Eric Yu

  4. EECS, University of Ottawa, Canada

    Daniel Amyot

Authors
  1. Jennifer Horkoff
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  2. Alex Borgida
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  3. John Mylopoulos
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  4. Daniele Barone
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  5. Lei Jiang
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  6. Eric Yu
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  7. Daniel Amyot
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Semantic Technology and Application Research Laboratory (STARLab), Vrije Universiteit Brussel, Building G-10, Pleinlaan 2, 1050, Brussels, Belgium

    Robert Meersman

  2. Research Centre for Automatic Control, School of Engineering in Information Technology, Campus scientifique, University of Lorraine, CNRS, BP 70239, 54506, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  3. La Trobe University, Melbourne, VIC, Australia

    Tharam Dillon

  4. Faculty of Computer Science, University of Vienna, 1010, Vienna, Austria

    Stefanie Rinderle-Ma

  5. Institute of Databases and Information Systems, Ulm University, Germany

    Peter Dadam

  6. School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  7. HP Labs, Bristol, UK

    Siani Pearson

  8. Johannes Kepler University, Linz, Austria

    Alois Ferscha

  9. Università di Modena e Reggio Emilia, Modena, Italy

    Sonia Bergamaschi

  10. ADVIS Lab, Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

    Isabel F. Cruz

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© 2012 Springer-Verlag Berlin Heidelberg

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Horkoff, J. et al. (2012). Making Data Meaningful: The Business Intelligence Model and Its Formal Semantics in Description Logics. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2012. OTM 2012. Lecture Notes in Computer Science, vol 7566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33615-7_17

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  • DOI: https://doi.org/10.1007/978-3-642-33615-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33614-0

  • Online ISBN: 978-3-642-33615-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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