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Metrics for Process Models [electronic resource] : Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness / by Jan Mendling.

Por: Tipo de material: TextoTextoSeries Lecture Notes in Business Information Processing ; 6 | Lecture Notes in Business Information Processing ; 6Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Descripción: online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9783540892243
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 005.7 23
Clasificación LoC:
  • QA76.76.A65
Recursos en línea:
Contenidos:
Springer eBooksResumen: Business process modeling plays an important role in the management of business processes. As valuable design artifacts, business process models are subject to quality considerations. The absence of formal errors such as deadlocks is of paramount importance for the subsequent implementation of the process. In his book Jan Mendling develops a framework for the detection of formal errors in business process models and the prediction of error probability based on quality attributes of these models (metrics). He presents a precise description of Event-driven Process Chains (EPCs), their control-flow semantics and a suitable correctness criterion called EPC soundness.
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Business Process Management -- Event-Driven Process Chains (EPC) -- Verification of EPC Soundness -- Metrics for Business Process Models -- Validation of Metrics as Error Predictors -- Implications for Business Process Modeling.

Business process modeling plays an important role in the management of business processes. As valuable design artifacts, business process models are subject to quality considerations. The absence of formal errors such as deadlocks is of paramount importance for the subsequent implementation of the process. In his book Jan Mendling develops a framework for the detection of formal errors in business process models and the prediction of error probability based on quality attributes of these models (metrics). He presents a precise description of Event-driven Process Chains (EPCs), their control-flow semantics and a suitable correctness criterion called EPC soundness.

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