Abstract
Business processes are crucial for every organisation as they represent the core value generating processes. Managing business processes is important to be efficient and to compete with a globalized market. Business process monitoring is an essential means to understand and to improve working procedures. It helps detecting deviations from planned procedures and brings transparency into the state and progress of running process instances. However, without automated execution of business processes via a workflow engine, the absence of execution information hampers monitoring. Often, the automated execution of business processes is neither feasible nor desirable. However, a few monitoring points can be used, when process participants interact with IT-systems.
In this paper, we propose a novel approach to business process monitoring using probabilistic estimations to fill information for missing monitoring points. The applicability of the approach is evaluated with a case study in a German university hospital.
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References
McCoy, D.W.: Business Activity Monitoring: Calm Before the Storm. Gartner Research Note LE-15-9727, vol. 15 (2002)
Dahanayake, A., Welke, R.J., Cavalheiro, G.: Improving the Understanding of BAM Technology for Real-Time Decision Support. International Journal of Business Information Systems 7, 1–26 (2011)
Rozinat, A., van der Aalst, W.M.P.: Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems 33, 64–95 (2008)
Lenz, R., Reichert, M.: IT Support for Healthcare Processes Premises, Challenges, Perspectives. Data & Knowledge Engineering 61, 39–58 (2007)
van der Aalst, W.M.P.: Verification of Workflow Nets. In: Application and Theory of Petri Nets 1997, pp. 407–426 (1997)
zur Muehlen, M., Recker, J.: How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 465–479. Springer, Heidelberg (2008)
Russell, N., van der Aalst, W.M.P., ter Hofstede, A.H.M., Edmond, D.: Workflow Resource Patterns: Identification, Representation and Tool Support. In: Pastor, Ó., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 216–232. Springer, Heidelberg (2005)
Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer-Verlag New York Inc. (2007)
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time Prediction Based on Process Mining. Information Systems 36, 450–475 (2011)
Hollingsworth, D., et al.: Workflow Management Coalition: The Workflow Reference Model. Workflow Management Coalition (1993)
Liu, X., Chen, J., Yang, Y.: A Probabilistic Strategy for Setting Temporal Constraints in Scientific Workflows. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 180–195. Springer, Heidelberg (2008)
Strum, D.P., May, J.H., Vargas, L.G.: Modeling the uncertainty of surgical procedure times: comparison of log-normal and normal models. Anesthesiology 92, 1160–1167 (2000)
Wu, J., Mehta, N., Zhang, J.: A Flexible Lognormal Sum Approximation Method. In: GLOBECOM 2005, pp. 3413–3417 (2005)
Wright, I.H., Kooperberg, C., Bonar, B.A., Bashein, G.: Statistical Modeling to Predict Elective Surgery Time – Comparison with a Computer Scheduling System and Surgeon-Provided Estimates (1996)
Rozinat, A., van der Aalst, W.M.P.: Decision Mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420–425. Springer, Heidelberg (2006)
Grigori, D., et al.: Business Process Intelligence. Computers in Industry 53, 321–343 (2004)
Kelley Jr., J.E., Walker, M.R.: Critical-Path Planning and Scheduling. Papers Presented at the Eastern Joint IRE-AIEE-ACM Computer Conference, December 1-3, pp. 160–173. ACM (1959)
Moder, J.J., Phillips, C.R.: Project Management with CPM and PERT (1964)
Martin, J.J.: Distribution of the Time through a Directed, Acyclic Network. Operations Research 13, 46–66 (1965)
Neumann, K.: Recent Advances in Temporal Analysis of GERT Networks. Zeitschrift für Operations Research 23, 153–177 (1979)
de Vin, L.J., Ng, A.H.C., Oscarsson, J.: Simulation-based Decision Support for Manufacturing System Life Cycle Management. Journal of Advanced Manufacturing Systems 3, 115–128 (2004)
de Vin, L.J., Ng, A.H.C., Oscarsson, J., Andler, S.F.: Information Fusion for Simulation based Decision Support in Manufacturing. Robotics and Computer-Integrated Manufacturing 22, 429–436 (2006)
Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)
Weijters, A., van der Aalst, W.M.P.: Process Mining: Discovering Workflow Models from Event-Based Data. In: BNAIC 2001, Citeseer, pp. 283–290 (2001)
van der Aalst, W.M.P.: Business Alignment: Using Process Mining as a Tool for Delta Analysis and Conformance Testing. Requirements Engineering Journal 10, 198–211 (2005)
van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle Time Prediction: When Will This Case Finally Be Finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)
González, O.: Monitoring and Analysis of Workflow Applications: A Domain-specific Language Approach Ph.D. thesis, Universidad de los Andes (2010)
Pedrinaci, C., et al.: SENTINEL: A Semantic Business Process Monitoring Tool. In: Proceedings of the First International Workshop on Ontology-Supported Business Intelligence, pp. 1–12. ACM (2008)
Pedrinaci, C., Markovic, I., Hasibether, F.: Strategy-Driven Business Process Analysis. In: Business Information, pp. 1–12 (2009)
Alves de Medeiros, A.K., Pedrinaci, C., van der Aalst, W.M.P., Domingue, J., Song, M., Rozinat, A., Norton, B., Cabral, L.: An Outlook on Semantic Business Process Mining and Monitoring. In: Meersman, R., Tari, Z. (eds.) OTM 2007 Ws, Part II. LNCS, vol. 4806, pp. 1244–1255. Springer, Heidelberg (2007)
DeFee, J.M., Harmon, P.: Business Activity Monitoring and Simulation. BP Trends Newsletter, White Paper and Technical Briefs, pp. 1–24 (2004)
Kang, B., Kim, D., Kang, S.: Periodic Performance Prediction for Real-time Business Process Monitoring. Industrial Management & Data (2011)
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Rogge-Solti, A., Weske, M. (2012). Enabling Probabilistic Process Monitoring in Non-automated Environments. In: Bider, I., et al. Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2012 2012. Lecture Notes in Business Information Processing, vol 113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31072-0_16
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DOI: https://doi.org/10.1007/978-3-642-31072-0_16
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