Huber S, Fietta M, Hof S (2015)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2015
Publisher: ACM
City/Town: New York
Book Volume: 0
Conference Proceedings Title: Proceedings of the S-BPM ONE '15
ISBN: 978-1-4503-3312-2
URI: http://doi.acm.org/10.1145/2723839.2723842
Adaptive Case Management (ACM) is a new paradigm that facilitates the coordination of knowledge work through case handling. Current ACM systems, however, lack support of providing sophisticated user guidance for next step recommendations and predictions about the case future. In recent years, process mining research developed approaches to make recommendations and predictions based on event logs readily available in process-aware information systems. This paper builds upon those approaches and integrates them into an existing ACM solution. The research goal is to design and develop a prototype that gives next step recommendations and predictions based on process mining techniques in ACM systems. The models proposed, recommend actions that shorten the case running time, mitigate deadline transgressions, support case goals and have been used in former cases with similar properties. They further give case predictions about the remaining time, possible deadline violations, and whether the current case path supports given case goals. A final evaluation proves that the prototype is indeed capable of making proper recommendations and predictions. In addition, starting points for further improvement are discussed.
APA:
Huber, S., Fietta, M., & Hof, S. (2015). Next Step Recommendation and Prediction based on Process Mining in Adaptive Case Management. In Proceedings of the S-BPM ONE '15. New York: ACM.
MLA:
Huber, Sebastian, Marian Fietta, and Sebastian Hof. "Next Step Recommendation and Prediction based on Process Mining in Adaptive Case Management." Proceedings of the Proceedings of the S-BPM ONE '15 New York: ACM, 2015.
BibTeX: Download