Abstract
Process mining techniques aim at extracting knowledge from event logs. One of the most important tasks in process mining is process model discovery. In discovering process models, an algorithm is designed to build a process model from a given event log. In this paper, a new model to discover process models has been proposed. A combination of Genetic Algorithm and Simulated Annealing has been used in this model. Genetic Algorithms has previously been used in this context. Previous approaches had drawbacks in fitness evaluation that misguided the algorithm. Another problem was that the quality of the candidates, in the population, was low such that it reduced the chance of finding a perfect answer. In this paper, a new fitness measure has been proposed to evaluate process models based on event logs. Moreover SA has been used to improve the quality of candidates in the population. It has been demonstrated that the proposed model outperformed in terms of rediscovering process models, compared to other approaches which are proposed in the literature, which was the result of better fitness evaluation and increased quality of individuals,. It came to conclusion that using GA and SA in combination with each other can be effective in this context.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Both Fiemke, Hoogendoorn Mark, Van der Mee Andy, Treur Jan, Vos Michael de (2012) An intelligent agent model with awareness of workflow progress. Appl Intell 36(2):498–510
Viara Popova, Alexei Sharpanskykh (2011) Formal analysis of executions of organizational scenarios based on process-oriented specifications. Appl Intell 34(2):226–244
Van der Aalst WMP (2011) Process Mining: discovery, Conformance and enhancement of business processes. Springer-Verlag, Berlin
Van der Aalst WMP, Alves de Medeiros AK , Weijters AJMM, Darondeau P (2005) Genetic Process Mining. In: Ciardo G (ed) Applications and theory of Petri Nets, Volume 3536 of lecture notes in computer science, pages 48-69. Springer-Verlag, Berlin
Alves de Medeiros A K, Weijters A J M M, Van der Aalst W M P (2007) Genetic process mining: an experimental evaluation, vol 14, pp 245–304
Alves de Medeiros AK (2006) Genetic process mining, dissertation. Eindhoven university of technology
Bratosin CC, Sidorova N, Van der Aalst WMP (2010) Discovering process models with genetic algorithms using sampling. In: Setchi R, Jordanov I, Howlett R J, Jain L C (eds) Knowledge-based and intelligent information and engineering systems (14th International Conference, KES’2010, Cardiff, UK, September 8-10, 2010. Proceedings). Springer, Berlin, pp 41–50
Bratosin CC, Sidorova N, Van der Aalst WMP (2010) Distributed genetic process mining. Proceedings IEEE world congress on computational intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23) pp. 1951–1958
Chieh-Yuan Tsai, Henyi Jen, Yi-Ching Chen (2010) Time-interval process model discovery and validation a genetic process mining approach. Appl Intell 33(1):54–66
Buijs JCAM, Van Dongen BF, Van der Aalst WMP (2012) A genetic algorithm for discovering process trees. IEEE congress on evolutionary computation (CEC)
Cook JE, Wolf AL (1995) Automating Process Discovery Through Event-Data Analysis In ICSE ’95: proceedings of the 17th international conference on Software engineering, pages 73-82. ACM Press, New York, NY, USA
Cook JE (1996) Process discovery and validation through event-data analysis, dissertation, University of Colorado
Cook JE, Wolf AL (1998) Discovering models of software processes from event-based data. ACM Trans Softw Eng Methodol 7(3):215–249
Agrawal R, Gunopulos D, Leymann F (1998) Mining process models from workflow logs. In: Ramos G, Alonso H.-J, Schek F Saltor (eds) Advances in database technology. EDBT’98: Sixth international conference on extending database technology, volume 1377 of lecture notes in computer science, pp 469–483
Van der Aalst WMP, Van Dongen BF, Herbst J, Maruster L, Schimm G, Weijters AJMM (2003) Workflow mining: a survey of issues and approaches. Data Knowl Eng 47(2):237–267
Van der Aalst WMP, Weijters AJMM, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng 16(9):1128–1142
Herbst J, Karagiannis D (2004) Workflow mining with InWoLvE. Comput Ind 53(3):245–264
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer-Verlag, Berlin
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Sci 220(4598):671–680
Acknowledgments
The authors wish to sincerely thank Mr. Hossein Abbasimehr for his help during this research and the writing of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vahedian Khezerlou, A., Alizadeh, S. A new model for discovering process trees from event logs. Appl Intell 41, 725–735 (2014). https://doi.org/10.1007/s10489-014-0564-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-014-0564-7