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Supporting Collaborative Work by Learning Process Models and Patterns from Cases

Title data

Schönig, Stefan ; Zeising, Michael ; Jablonski, Stefan:
Supporting Collaborative Work by Learning Process Models and Patterns from Cases.
In: 2013 9th International Conference on Collaborative Computing : Networking, Applications and Worksharing (Collaboratecom). Proceedings. - Piscataway, NJ , 2013 . - pp. 60-69
ISBN 978-1-936968-92-3
DOI: https://doi.org/10.4108/icst.collaboratecom.2013.254071

Abstract in another language

Recent work shows an increasing interest of the Busi-ness Process Management (BPM) community in unstructured, so-called "human-centric" processes. Case Management (CM) is a new trend that focuses on the support of collaborative human-cen-tric processes. Although CM provides concepts that support hu-man-centric work, processes have to be modelled beforehand in order to be supported by IT systems. Hence, a problem that arises when applying CM is that when organisations begin to formalize CM practice, it is often difficult to express rules controlling the ap-plicability of tasks. Furthermore, fundamental complexity chal-lenges arise when applying CM in practice. In this contribution, we provide a solution to these two issues. We propose that manag-ing human-centric processes should start with model skeletons that serve as a lattice where initial process execution can lean against. Additionally, by tracking different process cases, substan-tial process knowledge is recorded. Exploring process history might reveal certain recurring patterns that serve as dynamic guidance enhancement for CM systems. In this way, process mod-els might evolve over time, become more and more complete and better reflect operational reality.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Business Process Management; Process Mining; Adaptive Case Management; Association Rule Mining; Process Observation
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 07 Dec 2017 09:36
Last Modified: 07 Dec 2017 09:36
URI: https://eref.uni-bayreuth.de/id/eprint/34119