Transforming unstructured natural language descriptions into measurable process performance indicators using Hidden Markov Models

Aa, Han van der ; Leopold, Henrik ; Del-Rio-Ortega, Adela ; Resinas, Manuel ; Reijers, Hajo A.

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Document Type: Article
Year of publication: 2017
The title of a journal, publication series: Information Systems : IS
Volume: 71
Page range: 27-39
Place of publication: Amsterdam
Publishing house: Elsevier
ISSN: 0094-453X , 0306-4379
Publication language: English
Institution: School of Business Informatics and Mathematics > Methoden der künstlichen Intelligenz (Juniorprofessur) (van der Aa 2020-)
Subject: 004 Computer science, internet
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.

Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation.

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