Towards a Holistic Discovery of Decisions in Process-Aware Information Systems

The interest of integrating decision analysis approaches with the automated discovery of processes from data has seen a vast surge over the past few years. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group has provided a suitable solution for filling the void of decision representation in business process modeling languages. Process discovery has already embraced DMN for so-called decision mining, however, the efforts are still limited to a control flow point of view, i.e., explaining routing (constructs) or decision points. This work, however, introduces an integrated way of capturing the decisions that are embedded in the process, which is not limited to local characteristics, but provides a decision model in the form of a decision diagram which encompasses the full process execution span. Therefore, a typology is proposed for classifying different activities that contribute to the decision dimension of the process. This enables the possibility for an in-depth analysis of every activity, deciding whether it entails a decision, and what its relation is to other activities. The findings are implemented and illustrated on the 2013 BPI Challenge log, an exemplary dataset originating from a decision-driven process.

[1]  Jae-Yoon Jung,et al.  Constructing Decision Trees from Process Logs for Performer Recommendation , 2013, Business Process Management Workshops.

[2]  Hajo A. Reijers,et al.  Product Based Workflow Support: Dynamic Workflow Execution , 2008, CAiSE.

[3]  Hajo A. Reijers,et al.  Decision Mining Revisited - Discovering Overlapping Rules , 2016, CAiSE.

[4]  Sander J. J. Leemans,et al.  Discovering Block-Structured Process Models from Incomplete Event Logs , 2014, Petri Nets.

[5]  Johannes De Smedt,et al.  Consistent Integration of Decision (DMN) and Process (BPMN) Models , 2016, CAiSE Forum.

[6]  Johannes De Smedt,et al.  Business rules, decisions and processes: five reflections upon living apart together , 2013 .

[7]  Wil M. P. van der Aalst,et al.  Decision Mining in ProM , 2006, Business Process Management.

[8]  Wil M. P. van der Aalst,et al.  Data-aware process mining: discovering decisions in processes using alignments , 2013, SAC '13.

[9]  Boudewijn F. van Dongen,et al.  XES, XESame, and ProM 6 , 2010, CAiSE Forum.

[10]  Viara Popova,et al.  Artifact Lifecycle Discovery , 2013, Int. J. Cooperative Inf. Syst..

[11]  Wil M. P. van der Aalst,et al.  A General Framework for Correlating Business Process Characteristics , 2014, BPM.

[12]  Andreas Meyer,et al.  Extracting Decision Logic from Process Models , 2015, CAiSE.

[13]  Richard Hull,et al.  Introducing the Guard-Stage-Milestone Approach for Specifying Business Entity Lifecycles , 2010, WS-FM.

[14]  Hajo A. Reijers,et al.  Data-driven process discovery , 2017 .

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

[16]  Mathias Weske,et al.  Discovering Decision Models from Event Logs , 2016, BIS.

[17]  Johannes De Smedt,et al.  Decision Mining in a Broader Context: An Overview of the Current Landscape and Future Directions , 2016, Business Process Management Workshops.

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

[19]  Marlon Dumas,et al.  Discovering Branching Conditions from Business Process Execution Logs , 2013, FASE.

[20]  Irene T. P. Vanderfeesten,et al.  Making Decision Process Knowledge Explicit Using the Decision Data Model , 2011, BIS.

[21]  Daniel Mican,et al.  Making decision process knowledge explicit using the product data model , 2011 .