8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

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

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2012.250346,
        author={Stefan Sch\o{}nig and Michael Zeising and Stefan Jablonski},
        title={Adapting Association Rule Mining to Discover Patterns of Collaboration in Process Logs},
        proceedings={8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={IEEE},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={12},
        keywords={process mining data mining association rule mining business rules guidance through process execution},
        doi={10.4108/icst.collaboratecom.2012.250346}
    }
    
  • Stefan Schönig
    Michael Zeising
    Stefan Jablonski
    Year: 2012
    Adapting Association Rule Mining to Discover Patterns of Collaboration in Process Logs
    COLLABORATECOM
    ICST
    DOI: 10.4108/icst.collaboratecom.2012.250346
Stefan Schönig1,*, Michael Zeising1, Stefan Jablonski1
  • 1: University of Bayreuth
*Contact email: stefan.schoenig@uni-bayreuth.de

Abstract

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.