A Runtime Analysis of Graph-Theoretical Algorithms to Detect Patterns in Process Model Collections

Becker, Jörg; Breuker, Dominic; Delfmann, Patrick; Dietrich, Hanns-Alexander; Steinhorst, Matthias

doi:10.1007/978-3-642-36285-9_50

Cited by 3 publications

(2 citation statements)

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“…While many works applying NLP on conceptual models are concerned with model quality, there are also approaches which aim for eliciting knowledge that is implicitly captured by the model. Examples include the identification of activity correspondences between models [24,6], the discovery of services [46,52] and the elicitation of process patterns [32]. [60,16,8] or they ignore the syntax.…”

Section: State Of the Artmentioning

confidence: 99%

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Detection of naming convention violations in process models for different languages

Leopold

Eid-Sabbagh

Mendling

et al. 2013

Decision Support Systems

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Companies increasingly use business process modeling for documenting and redesigning their operations.However, due to the size of such modeling initiatives, they often struggle with the quality assurance of their model collections. While many model properties can already be checked automatically, there is a notable gap of techniques for checking linguistic aspects such as naming conventions of process model elements. In this paper, we address this problem by introducing an automatic technique for detecting violations of naming conventions. This technique is based on text corpora and independent of linguistic resources such as WordNet.Therefore, it can be easily adapted to the broad set of languages for which corpora exist. We demonstrate the applicability of the technique by analyzing nine process model collections from practice, including over 27,000 labels and covering three di↵erent languages. The results of the evaluation show that our technique yields stable results and can reliably deal with ambiguous cases. In this way, this paper provides an important contribution to the field of automated quality assurance of conceptual models.

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Section: State Of the Artmentioning

confidence: 99%

“…To accomplish this, each word of the input element is investigated in the context of a loop (lines [3][4][5][6]. If the considered word carries more than a single part of speech tag, the above introduced technique is used to decide on the actual part of speech (line 5).…”

Section: Ambiguity Resolutionmentioning

confidence: 99%