Transforming unstructured natural language descriptions into measurable process performance indicators using Hidden Markov Models. (November 2017) - van der Aa, Han; Leopold, Henrik; del-Río-Ortega, Adela; Resinas, Manuel; Reijers, Hajo A.
Highlights: We propose an approach to make natural language PPI descriptions measurable. The approach transforms unstructured descriptions into a structured notation. The fully automated approach builds on Hidden Markov Models to parse descriptions. Quantitative evaluation demonstrates the applicability of the approach in practice. Abstract: Monitoring process performance is an important means for organizations to identify opportunities to improve their operations. The definition of suitable Process Performance Indicators (PPIs) is a crucial task in this regard. Because PPIs need to be in line with strategic business objectives, the formulation of PPIs is a managerial concern. Managers typically start out to provide relevant indicators in the form of natural language PPI descriptions. Therefore, considerable time and effort have to be invested to transform these descriptions into PPI definitions that can actually be monitored. This work presents an approach that automates this task. The presented approach transforms an unstructured natural language PPI description into a structured notation that is aligned with the implementation underlying a business process. To do so, we combine Hidden Markov Models and semantic matching techniques. A quantitative evaluation on the basis of a data collection obtained from practice demonstrates that our approach works accurately. Therefore, it represents a viable automated alternative to an otherwise laborious manual endeavor.