Abstract
Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process models and are often only used to analyze a specific step in the overall process. Process mining focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques.
Process models are used for analysis (e.g., simulation and verification) and enactment by BPM/WFM systems. Previously, process models were typically made by hand without using event data. However, activities executed by people, machines, and software leave trails in so-called event logs. Process mining techniques use such logs to discover, analyze, and improve business processes.
Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active involvement of end-users, tool vendors, consultants, analysts, and researchers illustrates the growing significance of process mining as a bridge between data mining and business process modeling. The practical relevance of process mining and the interesting scientific challenges make process mining one of the “hot” topics in Business Process Management (BPM). This article introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto.
- Adriansyah, A., van Dongen, B., and van der Aalst, W. 2011. Conformance checking using cost-based fitness analysis. In Proceedings of the IEEE International Enterprise Computing Conference (EDOC’11). C. Chi and P. Johnson Eds., IEEE Computer Society, 55--64. Google ScholarDigital Library
- Agrawal, R., Gunopulos, D., and Leymann, F. 1998. Mining process models from workflow logs. In Proceedings of the 6th International Conference on Extending Database Technology. Lecture Notes in Computer Science, vol. 1377, Springer-Verlag, Berlin, 469--483. Google ScholarDigital Library
- Bergenthum, R., Desel, J., Lorenz, R., and Mauser, S. 2007. Process mining based on regions of languages. In Proceedings of the International Conference on Business Process Management (BPM’07). G. Alonso, P. Dadam, and M. Rosemann Eds., Lecture Notes in Computer Science, vol. 4714, Springer, 375--383. Google ScholarDigital Library
- Bose, R. P. J. C., van der Aalst, W., Zliobaite, I., and Pechenizkiy, M. 2011. Handling concept drift in process mining. In Proceedings of the International Conference on Advanced Information Systems Engineering (CAISE’11). H. Mouratidis and C. Rolland Eds., Lecture Notes in Computer Science, vol. 6741, Springer, 391--405. Google ScholarDigital Library
- Cook, J. and Wolf, A. 1998. Discovering models of software processes from event-based data. ACM Trans. Softw. Engin. Method. 7, 3, 215--249. Google ScholarDigital Library
- Cortadella, J., Kishinevsky, M., Lavagno, L., and Yakovlev, A. 1998. Deriving Petri nets from finite transition systems. IEEE Trans. Comput. 47, 8, 859--882. Google ScholarDigital Library
- Datta, A. 1998. Automating the discovery of as-is business process models: Probabilistic and algorithmic approaches. Inf. Syst. Resear. 9, 3, 275--301. Google ScholarDigital Library
- Desel, J. and Reisig, W. 1998. Place/Transition Nets. In Lectures on Petri Nets I: Basic Models, W. Reisig and G. Rozenberg Eds., Lecture Notes in Computer Science, vol. 1491, Springer-Verlag, Berlin, 122--173.Google Scholar
- Ehrenfeucht, A. and Rozenberg, G. 1989. Partial (set) 2-structures: Parts 1 Part 2. Acta Informatica 27, 4, 315--368.Google ScholarDigital Library
- Greco, G., Guzzo, A., Pontieri, L., and Saccà, D. 2006. Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Engin. 18, 8, 1010--1027. Google ScholarDigital Library
- Günther, C. and van der Aalst, W. 2007. Fuzzy mining: Adaptive process simplification based on multi-perspective metrics. In Proceedings of the International Conference on Business Process Management (BPM’07). G. Alonso, P. Dadam, and M. Rosemann Eds., Lecture Notes in Computer Science, vol. 4714, Springer-Verlag, Berlin, 328--343. Google ScholarDigital Library
- Hand, D., Mannila, H., and Smyth, P. 2001. Principles of Data Mining. MIT Press, Cambridge, MA. Google ScholarDigital Library
- Herbst, J. 2000. A machine learning approach to workflow management. In Proceedings of the 11th European Conference on Machine Learning. Lecture Notes in Computer Science, vol. 1810, Springer, 183--194. Google ScholarDigital Library
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. 2011. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.Google Scholar
- Medeiros, A., Weijters, A., and van der Aalst, W. 2007. Genetic process mining: An experimental evaluation. Data Mining Knowl. Discov. 14, 2, 245--304. Google ScholarDigital Library
- Munoz-Gama, J. and Carmona, J. 2011. Enhancing precision in process conformance: Stability, confidence and severity. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM’11). N. Chawla, I. King, and A. Sperduti Eds., IEEE.Google Scholar
- Rozinat, A. and van der Aalst, W. 2006. Decision mining in ProM. In Proceedings of the International Conference on Business Process Management (BPM’06). S. Dustdar, J. Fiadeiro, and A. Sheth Eds., Lecture Notes in Computer Science, vol. 4102, Springer, 420--425. Google ScholarDigital Library
- Rozinat, A. and van der Aalst, W. 2008. Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33, 1, 64--95. Google ScholarDigital Library
- Sole, M. and Carmona, J. 2010. Process mining from a basis of regions. In Applications and Theory of Petri Nets 2010. J. Lilius and W. Penczek Eds., Lecture Notes in Computer Science, vol. 6128, Springer 226--245. Google ScholarDigital Library
- Song, M. and van der Aalst, W. 2008. Towards comprehensive support for organizational mining. Dec. Support Syst. 46, 1, 300--317. Google ScholarDigital Library
- TFPM -- IEEE Task Force On Process Mining. 2011. Process mining manifesto. In Proceedings of the BPM Workshops. Lecture Notes in Business Information Processing Series, vol. 99, Springer.Google Scholar
- van der Aalst, W. 2011. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer. Google ScholarCross Ref
- van der Aalst, W. and Stahl, C. 2011. Modeling Business Processes: A Petri Net Oriented Approach. MIT Press, Cambridge, MA. Google ScholarDigital Library
- van der Aalst, W., van Dongen, B., Herbst, J., Maruster, L., Schimm, G., and Weijters, A. 2003. Workflow mining: A survey of issues and approaches. Data Knowl. Engin. 47, 2, 237--267. Google ScholarDigital Library
- van der Aalst, W., Weijters, A., and Maruster, L. 2004. Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Engin. 16, 9, 1128--1142. Google ScholarDigital Library
- van der Aalst, W., Reijers, H., Weijters, A., van Dongen, B., Medeiros, A., Song, M., and Verbeek, H. 2007. Business process mining: An industrial application. Inf. Syst. 32, 5, 713--732. Google ScholarDigital Library
- van der Aalst, W., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., and Günther, C. 2010. Process mining: A two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9, 1, 87--111.Google ScholarCross Ref
- van der Aalst, W., van Hee, K., Hofstede, A., Sidorova, N., Verbeek, H., Voorhoeve, M., and Wynn, M. 2011a. Soundness of workflow nets: Classification, decidability, and analysis. Formal Asp. Comput. 23, 3, 333--363. Google ScholarCross Ref
- van der Aalst, W., Schonenberg, M., and Song, M. 2011b. Time prediction based on process mining. Inf. Syst. 36, 2, 450--475. Google ScholarDigital Library
- van der Aalst, W., Adriansyah, A., and van Dongen, B. 2012. Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining Knowl. Discov. 2, 2, 182--192. Google ScholarDigital Library
- van Dongen, B. and van der Aalst, W. 2004. Multi-phase process mining: Building instance graphs. In Proceedings of the International Conference on Conceptual Modeling (ER’04). P. Atzeni, W. Chu, H. Lu, S. Zhou, and T. Ling Eds., Lecture Notes in Computer Science, vol. 3288, Springer, 362--376.Google Scholar
- van Dongen, B. and van der Aalst, W. 2005. Multi-phase mining: Aggregating instances graphs into EPCs and Petri nets. In Proceedings of the 2nd International Workshop on Applications of Petri Nets to Coordination, Workflow and Business Process Management. D. Marinescu Ed., 35--58.Google Scholar
- van Dongen, B., Busi, N., Pinna, G., and van der Aalst, W. 2007. An iterative algorithm for applying the theory of regions in process mining. In Proceedings of the Workshop on Formal Approaches to Business Processes and Web Services (FABPWS’07). W. Reisig, K. Hee, and K. Wolf Eds., Publishing House of University of Podlasie, Siedlce, Poland, 36--55.Google Scholar
- Verbeek, H., Buijs, J., van Dongen, B., and van der Aalst, W. 2010. ProM 6: The process mining toolkit. In Proceedings of BPM Demonstration Track 2010. M. L. Rosa Ed., CEUR Workshop Proceedings Series, vol. 615, 34--39.Google Scholar
- Weijters, A. and van der Aalst, W. 2003. Rediscovering workflow models from event-based data using Little Thumb. Integr. Comput.-Aid. Engin. 10, 2, 151--162. Google ScholarDigital Library
- Werf, J., Dongen, B. van, Hurkens, C., and Serebrenik, A. 2010. Process discovery using integer linear programming. Fundamenta Informaticae 94, 387--412. Google ScholarDigital Library
- Weske, M. 2007. Business Process Management: Concepts, Languages, Architectures. Springer. Google ScholarDigital Library
Index Terms
- Process Mining: Overview and Opportunities
Recommendations
Business process mining: An industrial application
Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although ...
Process Mining Put into Context
Process mining techniques help organizations discover and analyze business processes based on raw event data. The recently released "Process Mining Manifesto" presents guiding principles and challenges for process mining. Here, the authors summarize the ...
Service Mining: Using Process Mining to Discover, Check, and Improve Service Behavior
Web services are an emerging technology to implement and integrate business processes within and across enterprises. Service orientation can be used to decompose complex systems into loosely coupled software components that may run remotely. However, the ...
Comments