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The Business Intelligence Based Business Process Management Challenge

Author

Listed:
  • Oliver KOPF
  • Daniel HOMOCIANU

Abstract

The present paper is trying to bring some solid arguments concerning the common part, the relation and a few limitations when trying to bring together two recent fields of interest, namely Business Intelligence and Business Process Management. The approach is new only in terms of number and combination of reasons and the references are meant to support our ideas when trying to explain certain features. The entire content of the paper is based on putting together relevant theoretical ideas from the literature and some conclusions derived from the experience of authors and other industry representatives with different kinds of technologies and tools with many applications in these fields.

Suggested Citation

  • Oliver KOPF & Daniel HOMOCIANU, 2016. "The Business Intelligence Based Business Process Management Challenge," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 20(1), pages 7-19.
  • Handle: RePEc:aes:infoec:v:20:y:2016:i:1:p:7-19
    as

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    File URL: http://revistaie.ase.ro/content/77/01%20-%20Kopf,%20Homocianu.pdf
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    References listed on IDEAS

    as
    1. Elbashir, Mohamed Z. & Collier, Philip A. & Davern, Michael J., 2008. "Measuring the effects of business intelligence systems: The relationship between business process and organizational performance," International Journal of Accounting Information Systems, Elsevier, vol. 9(3), pages 135-153.
    2. Homocianu Daniel & Airinei Dinu, 2013. "Business Intelligence From E -Connotations to Technological Trends," Scientific Annals of Economics and Business, Sciendo, vol. 60(2), pages 1-9, December.
    3. Catalin STRIMBEI & Octavian DOSPINESCU & Roxana-Marina STRAINU & Alexandra NISTOR, 2015. "Software Architectures – Present and Visions," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(4), pages 13-27.
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    Cited by:

    1. Daniel Homocianu & Dinu Airinei, 2022. "PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets," Mathematics, MDPI, vol. 10(15), pages 1-27, July.

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