The increasing adoption of process-aware information systems (PAIS), together with the reuse of process knowledge, has resulted in process model repositories with large collections of process models. Understandability and maintainability of the process models in the repository are preconditions for their successful reuse. However, industrial process models display a wide range of quality problems impeding their comprehensibility and consequently hampering their maintainability and reuse. The literature reports, for example, error rates between 10% and 20% in industrial process model collections. Moreover, non-intention-revealing or inconsistent naming, redundant process fragments, and overly large and unnecessarily complex process models are typical quality problems that can be observed in existing process model collections.
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