Business Model Ontologies in OLAP Cubes

Christoph Georg Schütz; Bernd Neumayr; Michael Schrefl

doi:10.1007/978-3-642-38709-8_33

Business Model Ontologies in OLAP Cubes

Christoph Georg Schütz, Bernd Neumayr, Michael Schrefl

Institute of Business Informatics - Data & Knowledge Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologies.In this paper, we adopt business model ontologies for the representation of non-numeric measures in OLAP cubes. We propose modeling guidelines and adapt traditional OLAP operations for ontology-valued measures. Keywords: Business Intelligence, Business Modeling, Resource-Event-Agent, Resource Description Framework

Original language	English
Title of host publication	Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013
Editors	C. Salinesi, M. C. Norrie, O. Pastor
Publisher	Springer Verlag
Pages	514-529
Number of pages	16
Volume	7908
ISBN (Print)	978-3642387081
DOIs	https://doi.org/10.1007/978-3-642-38709-8_33
Publication status	Published - Jun 2013

Publication series

Name	Lecture Notes in Computer Science (LNCS)

Fields of science

102 Computer Sciences
102015 Information systems
502 Economics
509 Other Social Sciences

JKU Focus areas

Computation in Informatics and Mathematics
Management and Innovation

Access to Document

10.1007/978-3-642-38709-8_33

Cite this

Schütz, C. G., Neumayr, B., & Schrefl, M. (2013). Business Model Ontologies in OLAP Cubes. In C. Salinesi, M. C. Norrie, O. Pastor (Ed.), Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013 (Vol. 7908, pp. 514-529). (Lecture Notes in Computer Science (LNCS)). Springer Verlag. https://doi.org/10.1007/978-3-642-38709-8_33

Schütz, Christoph Georg ; Neumayr, Bernd ; Schrefl, Michael. / Business Model Ontologies in OLAP Cubes. Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013. editor / C. Salinesi, M. C. Norrie, O. Pastor. Vol. 7908 Springer Verlag, 2013. pp. 514-529 (Lecture Notes in Computer Science (LNCS)).

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abstract = "Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologies.In this paper, we adopt business model ontologies for the representation of non-numeric measures in OLAP cubes. We propose modeling guidelines and adapt traditional OLAP operations for ontology-valued measures. Keywords: Business Intelligence, Business Modeling, Resource-Event-Agent, Resource Description Framework",

author = "Sch{\"u}tz, \{Christoph Georg\} and Bernd Neumayr and Michael Schrefl",

year = "2013",

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doi = "10.1007/978-3-642-38709-8\_33",

language = "English",

isbn = "978-3642387081",

volume = "7908",

series = "Lecture Notes in Computer Science (LNCS)",

publisher = "Springer Verlag",

pages = "514--529",

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booktitle = "Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013",

}

Schütz, CG, Neumayr, B & Schrefl, M 2013, Business Model Ontologies in OLAP Cubes. in C. Salinesi, M. C. Norrie, O. Pastor (ed.), Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013. vol. 7908, Lecture Notes in Computer Science (LNCS), Springer Verlag, pp. 514-529. https://doi.org/10.1007/978-3-642-38709-8_33

Business Model Ontologies in OLAP Cubes. / Schütz, Christoph Georg; Neumayr, Bernd; Schrefl, Michael.
Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013. ed. / C. Salinesi, M. C. Norrie, O. Pastor. Vol. 7908 Springer Verlag, 2013. p. 514-529 (Lecture Notes in Computer Science (LNCS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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AB - Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologies.In this paper, we adopt business model ontologies for the representation of non-numeric measures in OLAP cubes. We propose modeling guidelines and adapt traditional OLAP operations for ontology-valued measures. Keywords: Business Intelligence, Business Modeling, Resource-Event-Agent, Resource Description Framework

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VL - 7908

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Schütz CG, Neumayr B, Schrefl M. Business Model Ontologies in OLAP Cubes. In C. Salinesi, M. C. Norrie, O. Pastor, editor, Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), Valencia, Spain, June 17-21, 2013. Vol. 7908. Springer Verlag. 2013. p. 514-529. (Lecture Notes in Computer Science (LNCS)). doi: 10.1007/978-3-642-38709-8_33

Business Model Ontologies in OLAP Cubes

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

SemCockpit - An Ontology-Driven, Interactive Business Intelligence Tool for Comparative Data Analysis

Cite this

Business Model Ontologies in OLAP Cubes

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

SemCockpit - An Ontology-Driven, Interactive Business Intelligence Tool for Comparative Data Analysis

Cite this