Abstract
Business process modeling is considered a manual, labor intensive task. It requires significant domain expertise and may be prone to errors or inconsistencies due to reliance on human factors. Hence, automation through reuse of predefined process models is becoming a common practice for generating new models. In this work we extend a previously proposed generation method by adding semantic learning capabilities that opt to improve the quality of generated business process models. The learning mechanism analyzes, in real-time, the linguistic relationships between process descriptors and adjusts them according to human inputs that are accumulated during the modeling process. To demonstrate the method we present a case-study from the food manufacturing industry. To estimate the applicative value we further experimented the method on a real-life process repository, showing that the learning mechanism increases the effectiveness of the previously suggested method for automating the design of new business process models.
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Wasser, A., Lincoln, M. (2012). Semantic Machine Learning for Business Process Content Generation. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2012. OTM 2012. Lecture Notes in Computer Science, vol 7565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33606-5_6
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DOI: https://doi.org/10.1007/978-3-642-33606-5_6
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