An Artificial Immune System Approach to Business Process Mining

Yong He Wei; Jun Zhong Wang

doi:10.4028/www.scientific.net/AMR.472-475.35

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 472-475An Artificial Immune System Approach to Business...

An Artificial Immune System Approach to Business Process Mining

Abstract:

The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process model. And the model’s structural complexity directly impacts readability and quality of the model. Immune systems have many characteristics such as uniqueness, autonomous, recognition of foreigners, distributed detection, and noise tolerance. This paper outlines an alternative approach to business process mining utilizing an artificial immune systems (AIS) technique, and some main steps and operators were depicted.

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Advanced Manufacturing Technology, ICMSE 2012

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Advanced Materials Research (Volumes 472-475)

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35-38

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https://doi.org/10.4028/www.scientific.net/AMR.472-475.35

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Online since:

February 2012

Authors:

Yong He Wei, Jun Zhong Wang

Keywords:

Artificial Immune System, Business Process, Process Mining

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References

[1] B. Weber, M. Reichert, S. Rinderle, and W. Wild: Proceedings of 1th Int'l Workshop on Business Process Intelligence(2005), p.191

Google Scholar

[2] C. J. Turner , A. Tiwari and J. Mehnen, :Proceedings of the 10th annual conference on Genetic and evolutionary computation(2008), p.1307

Google Scholar

[3] W. M. P. van der Aalst, A. J. M. M. Weijters: Computers in Industry Vol.53 (2004), p.231

Google Scholar

[4] W. M. P. van der Aalst, B. F. van Dongen, J. Herbst, L. Maruster, G. Schimm, A.J.M.M. Weijters: Data and Knowledge Engineering Vol. 47(2003), p.237

DOI: 10.1016/s0169-023x(03)00066-1

Google Scholar

[5] J. E. Cook and A. L. Wolf: ACM Transactions on Software Engineering and Methodology Vol. 7(1998), p.215

Google Scholar

[6] R. Agrawal, D. Gunopulos and F. Leymann: Proceedings of the 6th International Conference on Extending Database Technology (1998), p.469

Google Scholar

[7] A. de Medeiros, A. K., Genetic Process Mining, PhD Thesis, Eindhoven Technical University, Eindhoven, The Netherlands,2006.

Google Scholar

[8] L. de Castro and J. Timmis: Soft Computing Journal Vol. 7(2003), p.526

Google Scholar

[9] J.R. Al-Enezi, M.F. Abbod and S. Alsharhan: International Journal of Research and Reviews in Applied Sciences Vol.3(2010), p.118

Google Scholar

[10] F. Freschi and M. Repetto: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering Vol.25 (2006), p.792

Google Scholar

[11] Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P. Journal of Data Mining and Knowledge Discovery Vol.14(2007), p.245

Google Scholar