The Community for Technology Leaders
RSS Icon
Issue No.01 - Jan. (2013 vol.25)
pp: 192-205
Marc Solé , Technical University of Catalonia (UPC), Barcelona
Josep Carmona , Technical University of Catalonia (UPC), Barcelona
A central problem in the area of Process Mining is to obtain a formal model that represents the processes that are conducted in a system. If realized, this simple motivation allows for powerful techniques that can be used to formally analyze and optimize a system, without the need to resort to its semiformal and sometimes inaccurate specification. The problem addressed in this paper is known as Process Discovery: to obtain a formal model from a set of system executions. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets) from a set of traces. On its genuine form, the theory is applied on an automaton and therefore one should convert the traces into an acyclic automaton in order to apply these techniques. Given that the complexity of the region-based techniques depends on the size of the input automata, revealing the underlying cycles and folding the initial automaton can incur in a significant complexity alleviation of the region-based techniques. In this paper, we follow this idea by incorporating region information in the cycle detection algorithm, enabling the identification of complex cycles that cannot be obtained efficiently with state-of-the-art techniques. The experimental results obtained by the devised tool suggest that the techniques presented in this paper are a big step into widening the application of the theory of regions in Process Mining for industrial scenarios.
Petri nets, Data mining, Complexity theory, Proposals, Data structures, Noise, Learning automata, transition system folding, Process discovery, region theory
Marc Solé, Josep Carmona, "Region-Based Foldings in Process Discovery", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 1, pp. 192-205, Jan. 2013, doi:10.1109/TKDE.2011.192
[1] W. van der Aalst, H. Reijers, and M. Song, "Discovering Social Networks from Event Logs," Computer Supported Cooperative Work, vol. 14, no. 6, pp. 549-593, 2005.
[2] W. van der Aalst, T. Weijters, and L. Maruster, "Workflow Mining: Discovering Process Models from Event Logs," IEEE Trans. Knowledge Data Eng., vol. 16, no. 9, pp. 1128-1142, Sept. 2004.
[3] A. de Medeiros, W. van der Aalst, and A. Weijters, "Workflow Mining: Current Status and Future Directions," Proc. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, pp. 389-406, 2003.
[4] L. Wen, W. van der Aalst, J. Wang, and J. Sun, "Mining Process Models with Non-Free-Choice Constructs," Data Mining and Knowledge Discovery, vol. 15, no. 2, pp. 145-180, 2007.
[5] W. van der Aalst, A. de Medeiros, and A. Weijters, "Genetic Process Mining," Proc. 26th Int'l Conf. Applications and Theory of Petri Nets (ICATPN), pp. 48-69, 2005.
[6] A. Ehrenfeucht and G. Rozenberg, "Partial (Set) 2-Structures. Part I, II," Acta Informatica, vol. 27, pp. 315-368, 1990.
[7] E. Badouel, L. Bernardinello, and P. Darondeau, "Polynomial Algorithms for the Synthesis of Bounded Nets," Proc. Sixth Int'l Joint Conf. CAAP/FASE on Theory and Practice of Software Development, pp. 364-383, 1995.
[8] R. Bergenthum, J. Desel, R. Lorenz, and S. Mauser, "Synthesis of Petri Nets from Finite Partial Languages," Fundamental Information, vol. 88, no. 4, pp. 437-468, 2008.
[9] J. Cortadella, M. Kishinevsky, L. Lavagno, and A. Yakovlev, "Deriving Petri Nets from Finite Transition Systems," IEEE Trans. Computers, vol. 47, no. 8, pp. 859-882, Aug. 1998.
[10] J. Carmona, J. Cortadella, and M. Kishinevsky, "New Region-Based Algorithms for Deriving Bounded Petri Nets," IEEE Trans. Computers, vol. 59, no. 3, pp. 371-384, Mar. 2010.
[11] H. Verbeek, A. Pretorius, W. van der Aalst, and J. van Wijk, "On Petri-Net Synthesis and Attribute-Based Visualization," Proc. Workshop Petri Nets and Software Eng. (PNSE), pp. 127-141, 2007.
[12] W. van der Aalst, V. Rubin, H. Verbeek, B. van Dongen, E. Kindler, and C. Günther, "Process Mining: A Two-Step Approach to Balance between Underfitting and Overfitting," Software and Systems Modeling, vol. 9, pp. 87-111, 2010.
[13] J. Carmona, J. Cortadella, and M. Kishinevsky, "A Region-Based Algorithm for Discovering Petri Nets from Event Logs," Proc. Sixth Int'l Conf. Business Process Management (BPM), pp. 358-373, 2008.
[14] R. Bergenthum, J. Desel, R. Lorenz, and S. Mauser, "Process Mining Based on Regions of Languages," Proc. Fifth Int'l Conf. Business Process Management (BPM), pp. 375-383, 2007.
[15] J. van der Werf, B. van Dongen, C. Hurkens, and A. Serebrenik, "Process Discovery Using Integer Linear Programming," Proc. 29th Int'l Conf. Applications and Theory of Petri Nets, pp. 368-387, 2008.
[16] M. Solé and J. Carmona, "Process Mining from a Basis of State Regions," Proc. 31st Int'l Conf. Applications and Theory of Petri Nets (ATPN), pp. 226-245, 2010.
[17] T. Murata, "Petri Nets: Properties, Analysis and Applications," Proc. IEEE, vol. 77, no. 4, pp. 541-580, Apr. 1989.
[18] J. Desel and W. Reisig, "The Synthesis Problem of Petri Nets," Acta Informatica, vol. 33, no. 4, pp. 297-315, 1996.
[19] M. Mukund, "Petri Nets and Step Transition Systems," Foundations of Comp. Science, vol. 3, no. 4, pp. 443-478, 1992.
[20] L. Bernardinello, G.D. Michelis, K. Petruni, and S. Vigna, "On the Synchronic Structure of Transition Systems," Proc. Int'l Workshop Structures in Concurrency Theory, pp. 69-84, 1995.
[21] N. Busi and G.M. Pinna, "Process Discovery and Petri Nets," Math. Structures in Computer Science, vol. 19, no. Special Issue 06, pp. 1091-1124, 2009.
[22] E. Badouel and P. Darondeau, "Theory of Regions," Lectures on Petri Nets I: Basic Models, pp. 529-586, Springer, 1998.
[23] J. Jansson and Z. Peng, "Online and Dynamic Recognition of Squarefree Strings," Math. Foundation of Comp. Science, vol. 3618/2005, pp. 520-531, 2005.
[24] R. Kolpakov and G. Kucherov, "Finding Maximal Repetitions in a Word in Linear Time," Proc. 40th Ann. Symp. Foundations of Computer Science, pp. 596-604, 1999.
[25] M.G. Main and R.J. Lorentz, "An o(n log n) Algorithm for Finding all Repetitions in a String," J. Algorithms, vol. 5, no. 3, pp. 422-432, 1984.
[26] A. Apostolico and F.P. Preparata, "Optimal Off-Line Detection of Repetitions in a String," Theoretical Computer Science, vol. 22, no. 3, pp. 297-315, 1983.
[27] M. Crochemore, "Transducers and Repetitions," Theoretical Computer Science, vol. 45, pp. 63-86, 1986.
[28] D. Gusfield and J. Stoye, "Linear Time Algorithms for Finding and Representing all the Tandem Repeats in a String," J. Computer and System Sciences, vol. 69, no. 4, pp. 525-546, 2004.
[29] R. Jagadeesh Chandra Bose and W. van der Aalst, "Abstractions in Process Mining: A Taxonomy of Patterns," Business Process Management, vol. 5701, pp. 159-175, 2009.
[30] R.P.J.C. Bose and W.M.P. van der Aalst, "Context Aware Trace Clustering: Towards Improving Process Mining Results," Proc. SIAM Int'l Conf. Data Mining (SDM), pp. 401-412, 2009.
[31] J. Li, R.P.J.C. Bose, and W.M. van der Aalst, "Mining Context-Dependent and Interactive Business Process Maps Using Execution Patterns," Proc. Sixth Int'l Workshop Business Process Intelligence (BPM), vol. 66, pp. 109-121, 2011.
[32] W. van der Aalst, B. van Dongen, C. Günther, R. Mans, A. de Medeiros, A. Rozinat, V. Rubin, M. Song, H. Verbeek, and A. Weijters, "ProM 4.0: Comprehensive Support for Real Process Analysis," Proc. 28th Int'l Conf. Applications and Theory of Petri Nets and Other Models of Concurrency (ICATPN), pp. 484-494, 2007.
[33] M. Solé and J. Carmona, "Region-Based Foldings in Process Discovery," UPC, Technical Report UPC-DAC-RR-GEN-2010-1, 2010.
[34] A. Schrijver, Theory of Linear and Integer Programming. John Wiley & Sons, 1986.
[35] G. Greco, A. Guzzo, L. Pontieri, and D. Saccà, "Discovering Expressive Process Models by Clustering Log Traces," IEEE Trans. Knowledge Data Eng., vol. 18, no. 8, pp. 1010-1027, Aug. 2006.
[36] L. Ghionna, G. Greco, A. Guzzo, and L. Pontieri, "Outlier Detection Techniques for Process Mining Applications," Foundations of Intelligent Systems, vol. 4994, pp. 150-159, 2008.
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool