Transforming unstructured natural language descriptions into measurable process performance indicators using Hidden Markov Models
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 for...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/131138 |
| Acceso en línea: | https://hdl.handle.net/11441/131138 https://doi.org/10.1016/j.is.2017.06.005 |
| Access Level: | acceso abierto |
| Palabra clave: | Performance Measurement Process performance indicators Natural language processing Hidden Markov Models Model alignment |
| Sumario: | 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 |
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