Assisting Users During Process Execution Through Recommendations
In today’s fast changing business environment, flexible Process-aware Information Systems (PAISs) are required to allow companies to rapidly adjust their business processes to changes in the environment . Several proposals on how to deal with this challenge have been made (e.g., [2, 3, 4, 5]) relaxing the strict separation of build-time and run-time. By closely interweaving modeling and execution the above mentioned approaches all provide more maneuvering room for the end-users . In particular, users are empowered to defer decisions regarding the exact control-flow to run-time, when more information is available.
With this increase of flexibility, however, additional challenges are imposed to the users of flexible PAISs. A recently performed experiment at the University of Innsbruck shows that with increased flexibility users with little experience have greater difficulties during process execution, which in the worst case may result in process instances that cannot be properly completed . This trade-off between flexibility and support is also described in .
To address the above mentioned challenges and to assist users during process execution, we are working on intelligent user assistance in flexible PAISs [8, 9]. In particular, we proposed a recommendation service (including an implementation in ProM) which exploits the information available in event logs to guide users during process execution. The recommendation service provides information to users of a flexible PAIS on how to best proceed with a particular process instance depending on the execution state of that instance to best achieve a certain performance goal (e.g., minimizing cycle time, or maximizing profit).
Recommendations Based on Optimized Enactment Plans
In addition to log-based recommendations we proposed an approach for generating recommendations from optimized enactment plans which assists users during process execution to optimize performance goals of the processes. The recommendation system is based on a constraint-based approach for planning and scheduling the BP activities and considers both the control-flow and the resource perspective (for details see ).
- I. Barba, B. Weber and C. Del Valle: Supporting the Optimized Execution of Business Processes through Recommendations Considering Resources. In: Proc. BPI ’11 (accepted), 2011.
- C. Haisjackl and B. Weber: User Assistance During Process Execution – An Experimental Evaluation of Recommendation Strategies. In: BPI 2010 Workshop, pp. 134–145, 2010.
- H. Schonenberg, B. Weber, B. van Dongen and W. van der Aalst: Supporting Flexible Processes Through Log-Based Recommendations.. In: BPM’08, pp. 51–66, 2008.
 v.d. Aalst, W., Jablonski, S.: Dealing with Worflkow Change: Identification of Issues an Solutions. IJCSES 15(5) (2000) 267–276.
 Reichert, M., Dadam, P.: ADEPTflex – Supporting Dynamic Changes of Work-flows Without Losing Control. JIIS 10(2) (1998) 93–129.
 Sadiq, S., Sadiq, W., Orlowska, M.: A Framework for Constraint Specification and Validation in Flexible Workows. Information Systems 30(5) (2005) 349–378.
. Pesic, M., Schonenberg, M., Sidorova, N., v. d. Aalst, W.: Constraint-Based Work-flow Models: Change Made Easy. In: CoopIS’07. (2007) 77–94.
 van der Aalst, W., Weske, M., Grünbauer, D.: Case Handling: A New Paradigm for Business Process Support. DKE 53(2) (2005) 129–162.
 Zugal, S.: Agile versus Plan-Driven Approaches to Planning – A Controlled Experiment. Master’s thesis, University of Innsbruck (October 2008).
 Dongen, B., Aalst, W.: A meta model for process mining data. In: Proc. CAiSE WORKSHOPS. (2005) 309–320.
 Schonenberg, H.,Weber, B., van Dongen, B., van der Aalst, W.: Supporting flexible processes through log-based recommendations. In: Proc. BPM’08. (2008) 51–66.
 Haisjackl, C., Weber, B.: User Assistance during Process Execution – An Experimental Evaluation of Recommendation Strategies. Business Process Management Workshops 2010: 134–145.
 Irene Barba, Barbara Weber and Del Valle, C.: Supporting the Optimized Execution of Business Processes through Recommendations Considering Resources. In: BPI (accepted), 2011.