Advertisement

A Classification Algorithm for Assessing the Quality Criteria for Business Process Models

  • Fouzia Kahloun
  • Sonia Ayachi Ghannouchi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)

Abstract

Business process (BP) models are presented as a major key in the design and analysis of information systems and are considered as a good mechanism for communication among the stakeholders. Therefore, it should be founded on excellence methodologies and directed by a reliable quality approach. That is why, it is important to define the quality of BP models, which should be determined the application of a set of criteria. In this paper, we look for identify a set of typical and consistent criteria considered as substantial, for BP models by focusing on the needs of stakeholders with the aim of achieving good quality. These requirements are established by first passing through a preliminary study that is based on a questionnaire designed in order to assess the importance attributed to the mentioned criteria. The responses given to this questionnaire will be analyzed with an algorithm of classification for data mining in order to identify the quality criteria from the expert’s point of view, given their relative importance.

Keywords

Quality Criteria Business process model 

References

  1. 1.
    Gokçen, T.: Evaluating the complexity of business process models (2011)Google Scholar
  2. 2.
    Kahloun, F., Ghannouchi, S.A.: Quality criteria and metrics for business process models in higher education domain: case of a tracking of curriculum offers process. In: International conference on Enterprise Information Systems (2016)Google Scholar
  3. 3.
    Taft, T., Duff, R.A., Brukardt, R.L., Pascal, E.L.: Then International Standars Organization ISO 8601 (1998)Google Scholar
  4. 4.
    Henry, S., Kafura, D.: Software structure metrics based on information-flow. IEEE Trans. Softw. Eng. 7(5), 510–518 (1981)Google Scholar
  5. 5.
    Sanchez, G.L., Garcia, F., Piattini, M.: Toward a quality framework for business process models. Int. J. Coop. Inf. Syst. 22(01), 1349003 (2013)Google Scholar
  6. 6.
    Kluza, K., Nalepa, G.J.: Proposal of square metrics for measuring business process model complexity. In: 2012 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 919–922. IEEE (2012)Google Scholar
  7. 7.
    Cardoso, J.: Process control-flow complexity metric: an empirical validation. In: IEEE International Conference on Services Computing (SCC 2006), pp. 167–173 (2006)Google Scholar
  8. 8.
    Cardoso, J., Mendling, J., Neumann, G., Reijers, H.A: A discourse on complexity of process models. In: International Conference on Business Process Management, pp. 117–128 (2006)Google Scholar
  9. 9.
    Latva- Koivisto, A.M.: Finding a complexity measure for business process models. Helsinki University of Technology, Systems Analysis Laboratory (2001)Google Scholar
  10. 10.
    Corti, C.: BPMETRICS: a software system for the evaluation of some metrics for business process (2012)Google Scholar
  11. 11.
    Sadowska, M.: An approach to assessing the quality of business process models expressed in BPMN. e- Informatica Softw. Eng. J. 9, 57–77 (2015)Google Scholar
  12. 12.
    Antonini, A., Ferreira, A.M, Morasca, S., Pozzi, G.: Software measures for business processes. In: Advances in Databases and Information Systems (2011)Google Scholar
  13. 13.
    Gruhn, V., Laue, L.: Complexity metrics for business process models. In: 9th International Conference on Business Information Systems (BIS 2006), pp. 1–12 (2006)Google Scholar
  14. 14.
    Khlif, W., Makni, L., Zaaboub, N., Ben-Abdallah, H.: Quality metrics for business process modeling. In: Proceedings of the 9th WSEAS International Conference on Applied Computer Science. World Scientific and Engineering Academy and Society (WSEAS), pp. 195–200 (2009)Google Scholar
  15. 15.
    Vanderfeesten, I., Reijers, H.A., Mendling, J., van der Aalst, W.M., Cardoso, J.: On a quest for good process models: the cross-connectivity metric. In: Advanced Information Systems Engineering, pp. 480–494. Springer (2008)Google Scholar
  16. 16.
    Cardoso, J., Vanderfeesten, I., Reijers, H.A.: Computing coupling for business process models (2010). http://eden.dei.uc.pt/jcardoso/Research/Papers/Old%20paper%20format/Caise-19th-Coupling-Cardoso-Vanderfeesten.pdf>
  17. 17.
    Kahloun, F., Ayachi, S.A.: Evaluating the quality of business process models based on measures and criteria in higher education developing a framework for continuous quality improvement. In: ISDA Conference (2016)Google Scholar
  18. 18.
    Vanderfeesten, I., Cardoso, J., Reijers, H.A.: A weighted coupling metric for business process models. CAiSE Forum (2007)Google Scholar
  19. 19.
    Kherbouche, M.O.:Contribution à la gestion de l’évolution des processus métiers. In: Laboratoire d’informatique signal et image de la Cote d Opale. Université du Littoral Cote d Opale, p. 195 (2013)Google Scholar
  20. 20.
    Farideh, H., Pericles, L.: Quality evaluation framework (QEF): modeling and evaluating quality of business processes. Int. J. Acc. Inf. Syst. 15(3), 193–223 (2014)Google Scholar
  21. 21.
    Omg: UMLTM profile for modeling quality of service and fault tolerance characteristics and mechanisms specification2.1 ed.; [formal/2008-04-05] (2008)Google Scholar
  22. 22.
    van Hee, K., Reijers, H.: Using formal analysis techniques in business process redesign. In: Business Process Management, pp. 51–71 (2000)Google Scholar
  23. 23.
    Li, G., Muthusamy, V., Jacobsen, H.A.: A distributed service-oriented architecture for business process execution. ACM Trans. Web (TWEB) 4(1), 2 (2010)Google Scholar
  24. 24.
    Moeller, R.: Sarbanes-Oxley Internal Controls: Effective Auditing with AS5, CobiT and ITIL. John Wiley & Sons, New Jersey (2008)Google Scholar
  25. 25.
    Laird, L.M., Brennan, M.C.: Software Measurement and Estimation: A Practical Approach. John Wiley & Sons, Hoboken (2006)CrossRefGoogle Scholar
  26. 26.
    Kahloun, F., Ayachi Ghannouchi, S.: Evaluation of the criteria and indicators that determine quality in higher education: a questionnaire proposal. In: International Conference on Intelligent Interactive Multimedia Systems and Services. Springer, Cham (2017)Google Scholar
  27. 27.
    Morin, V.: Etude comparative d’algorithmes de data mining dans le contexte du jeu vidéo (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory RIADI-GDLENSIMannoubaTunisia
  2. 2.High Institute on Management of SousseSousseTunisia

Personalised recommendations