Large corporations increasingly utilize business process models for documenting and redesigning their
operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly
trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models,
in particular, its activity labels. In this paper, we address the problem of activity label quality in business
process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser.
Using three business process model collections from practice with differing labeling style distributions, we
demonstrate the applicability of our technique. In comparison to a straightforward application of standard
natural language tools, our technique provides much more stable results. As an outcome, the technique shifts
the boundary of process model quality issues that can be checked automatically from syntactic to semantic
aspects.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3496 |
| Date | 14 January 2012 |
| Creators | Leopold, Henrik, Smirnov, Sergey, Mendling, Jan |
| Publisher | Elsevier |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://dx.doi.org/10.1016/j.is.2012.01.004, http://www.elsevier.com, http://epub.wu.ac.at/3496/ |
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