An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement

Process mining (PM) is a technique to extract a process model from an event log to represent the process behaviour recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behaviour recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement and benchmarking. Genetic process mining (GPM) is a famous PM approach, which can simultaneously identify several process structures from event logs. However, GPM cannot effectively discover parallel structures from event logs. This study proposes a PM approach based on integration of GPM, particle swarm optimisation and differential evolution to find process models with high fitness for event logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.

[1]  Wil M. P. van der Aalst,et al.  Genetic process mining: an experimental evaluation , 2007, Data Mining and Knowledge Discovery.

[2]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[3]  Aphrodite Tsalgatidou,et al.  Multilevel Petri Nets for Modeling and Simulating Organizational Dynamic Behavior , 1996 .

[4]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[5]  Andries Petrus Engelbrecht,et al.  Differential Evolution Based Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[6]  Wil M. P. van der Aalst,et al.  Process Mining Techniques: an Application to Stroke Care , 2008, MIE.

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Boudewijn F. van Dongen,et al.  Business process mining: An industrial application , 2007, Inf. Syst..

[9]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  Gerrit Tamm,et al.  Optimization of Service Delivery through Continual Process Improvement: A Case Study , 2010, ISSS/BPSC.

[11]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[12]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[13]  Andries Petrus Engelbrecht,et al.  Bare bones differential evolution , 2009, Eur. J. Oper. Res..

[14]  Michael Westergaard,et al.  CPN Tools for Editing, Simulating, and Analysing Coloured Petri Nets , 2003, ICATPN.

[15]  Andrea Sackmann,et al.  Petri net based model of the body iron homeostasis , 2007, J. Biomed. Informatics.

[16]  Jacques Wainer,et al.  Anomaly Detection Using Process Mining , 2009, BMMDS/EMMSAD.

[17]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  D. Salari,et al.  Employing the Taguchi method to obtain the optimum conditions of coagulation-flocculation process in tannery wastewater treatment , 2010 .

[20]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[21]  Sooyoung Yoo,et al.  Discovery of Outpatient Care Process of a Tertiary University Hospital Using Process Mining , 2013, Healthcare informatics research.

[22]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[23]  Michal R. Przybylek.  Skeletal Algorithms in Process Mining , 2013 .

[24]  Ajith Abraham,et al.  DE-PSO: A NEW HYBRID META-HEURISTIC FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS , 2011 .

[25]  Xingsheng Gu,et al.  A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan ☆ , 2008 .

[26]  Liang Gao,et al.  An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers , 2011, Inf. Sci..

[27]  R. J. Kuo,et al.  Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering , 2010, Decis. Support Syst..

[28]  Wil M. P. van der Aalst,et al.  Process Mining Applied to the Test Process of Wafer Scanners in ASML , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[29]  Chin-Teng Lin,et al.  Ga-based Reinforcement Learning for Neural Networks , 1998, Int. J. Syst. Sci..

[30]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2006, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[31]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[32]  Jianmin Wang,et al.  Mining process models with non-free-choice constructs , 2007, Data Mining and Knowledge Discovery.

[33]  Fatos Xhafa,et al.  Use of genetic algorithms for scheduling jobs in large scale grid applications , 2006 .

[34]  Boudewijn F. van Dongen,et al.  On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery , 2012, OTM Conferences.

[35]  Tim Hendtlass,et al.  A Combined Swarm Differential Evolution Algorithm for Optimization Problems , 2001, IEA/AIE.

[36]  John Ryan,et al.  Process modeling for simulation , 2006, Comput. Ind..

[37]  San-Yih Hwang,et al.  A process-mining framework for the detection of healthcare fraud and abuse , 2006, Expert Syst. Appl..

[38]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[39]  Mathias Weske,et al.  Business Process Management: Concepts, Languages, Architectures , 2007 .

[40]  Boudewijn F. van Dongen,et al.  The ProM Framework: A New Era in Process Mining Tool Support , 2005, ICATPN.

[41]  Jigui Sun,et al.  An improved particle swarm optimization algorithm for flowshop scheduling problem , 2008, 2008 International Conference on Information and Automation.

[42]  Jan Mendling,et al.  Declarative versus Imperative Process Modeling Languages: The Issue of Understandability , 2009, BMMDS/EMMSAD.

[43]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.