Abstract
While executing business processes, regularly decisions need to be made such as which activities to execute next or what kind of resource to assign to a task. Such a decision-making process is often case-dependent and carried out under uncertainty, yet requiring compliance with organization’s service level agreements. In this paper, we address these challenges by presenting an approach for dynamic decision-making. It is able to automatically propose case-dependent decisions during process execution. Finally, we evaluate it with a use case that highlights the improvements of process executions based on our dynamic decision-making approach.
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 318275 (GET Service).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Batoulis, K.: Proactive decision support during business process execution. In: EMoV+MinoPro@Modellierung, pp. 35–41 (2014)
Batoulis, K., Meyer, A., Bazhenova, E., Decker, G., Weske, M.: Extracting decision logic from process models. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 349–366. Springer, Heidelberg (2015)
Becker, J., Breuker, D., Delfmann, P., Matzner, M.: Designing and implementing a framework for event-based predictive modelling of business processes. In: EMISA, pp. 71–84 (2014)
Boyer, J., Mili, H.: Agile Business Rule Development - Process, Architecture, and JRules Examples. Springer, Heidelberg (2011)
Breuker, D., Delfmann, P., Matzner, M., Becker, J.: Designing and evaluating an interpretable predictive modeling technique for business processes. In: DeMiMoP (2014)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)
Debevoise, T., Taylor, J.: The Microguide to Process and Decision Modeling in BPMN/DMN. CreateSpace Independent Publishing Platform (2005)
Dumas, M., van der Aalst, W., ter Hofstede, A.: Process-Aware Information Systems. Wiley, New York (2005)
Engel, Y., Etzion, O., Feldman, Z.: A Basic Model for Proactive Event-driven Computing. In: ACM, DEBS (2012)
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.C.: Business process intelligence. Comput. Indus. 53(3), 321–343 (2004)
ISO: IEC 27002: 2013 (EN) Information technology-Security techniques - Code of practice for information security controls, Switzerland (2013)
Lakshmanan, G., Shamsi, D., Doganata, Y., Unuvar, M., Khalaf, R.: A Markov prediction model for data-driven semi-structured business processes. KAIS 42(1), 97–126 (2015)
Lauritzen, S.L., Nilsson, D.: Representing and solving decision problems with limited information. Manage. Sci. 47, 1235–1251 (2001)
Murphy, K.P.: The Bayes net toolbox for MATLAB. Comput. Sci. Stat. 33, 2001 (2001)
Neapolitan, R.: Learning Bayesian Networks. Prentice Hall, Upper Saddle River (2004)
OMG: Business Process Model and Notation (BPMN), Version 2.0, January 2011
OMG: Decision Model And Notation (DMN), Version 1.0 - Beta 1, February 2014
Rozinat, A., Aalst, W.M.P.: Decision mining in business processes. Beta, Research School for Operations Management and Logistics (2006)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall Press, Upper Saddle River (2009)
Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42(3), 1–42 (2010)
Sutrisnowati, R.A., Bae, H., Park, J., Ha, B.H.: Learning Bayesian network from event logs using mutual information test. In: SOCA, pp. 356–360 (2013)
Thullner, R., Rozsnyai, S., Schiefer, J., Obweger, H., Suntinger, M.: Proactive business process compliance monitoring with event-based systems. In: EDOCW, pp. 429–437. IEEE (2011)
United States Code: Sarbanes-Oxley Act of 2002, PL 107–204, 116 Stat 745. Codified in Sections 11, 15, 18, 28, and 29 USC, Jul 2002
Weske, M.: Business Process Management - Concepts, Languages, Architectures, 2nd edn. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Batoulis, K., Baumgraß, A., Herzberg, N., Weske, M. (2016). Enabling Dynamic Decision Making in Business Processes with DMN. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-42887-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42886-4
Online ISBN: 978-3-319-42887-1
eBook Packages: Computer ScienceComputer Science (R0)