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Adapting Association Rule Mining to Discover Patterns of Collaboration in Process Logs

Title data

Schönig, Stefan ; Zeising, Michael ; Jablonski, Stefan:
Adapting Association Rule Mining to Discover Patterns of Collaboration in Process Logs.
In: Carminati, Barbara (ed.): 8th International Conference on Collaborative Computing : Networking, Applications and Worksharing (Collaboratecom 2012). - Piscataway, NJ , 2012 . - pp. 531-534
ISBN 9781467327404
DOI: https://doi.org/10.4108/icst.collaboratecom.2012.250346

Abstract in another language

The execution order of work steps within business processes is influenced by several factors, like the organizational position of performing agents, document flows or temporal dependencies. Lately, process mining techniques are more and more successfully used to discover execution orders from process execution logs automatically. Although, these techniques have been applied in various domains, the methods are mostly discovering the execution order of process steps without facing possible coherency with other perspectives of business processes, i.e., other types of process execution data. The reasons, e.g., for a given execution order, remain mostly undiscovered. In this paper, we propose a method to discover cross-perspective collaborative patterns in process logs and therefore strive for a genotypic anal-ysis of recorded process data. For this purpose, we adapted the association rule mining algorithm to analyse execution logs. The resulting rules can be used for guiding users through collabora-tive process execution.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: organisational aspects; business data processing; data mining; document handling; groupware
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV
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
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 07 Dec 2017 10:11
Last Modified: 07 Dec 2017 10:11
URI: https://eref.uni-bayreuth.de/id/eprint/34900