Búsqueda avanzada

El autor Alfonso E. Márquez-Chamorro ha publicado 4 artículo(s):

1 - Towards a general architecture for predictive monitoring of business processes

Process mining allows the extraction of useful information from event logs and historical data of business processes. This information will improve the performance of these processes and is generally obtained after they have finished. Therefore, predictive monitoring of business process running instances is needed, in order to provide proactive and corrective actions to improve the process performance and mitigate the possible risks in real time. This monitoring allows the prediction of evaluation metrics for a runtime process. In this context, this work describes a general methodology for a business process monitoring system for the prediction of process performance indicators and their stages, such as, the processing and encoding of log events, the calculation of aggregated attributes or the application of a data mining algorithm.

Autores: Alfonso E. Márquez-Chamorro / Manuel Resinas / Antonio Ruiz-Cortés / 
Palabras Clave: Business process - business process indicator prediction - predictive monitoring - Process Mining

2 - Monitorización predictiva de procesos de negocio basada en modelos de predicción actualizables

La monitorización predictiva de instancias de procesos de negocio en ejecución propociona acciones proactivas y correctivas para mejorar el rendimiento de los procesos y mitigar los posibles riesgos en tiempo real. Dicha monitorización permite la predicción de métricas de evaluación o indicadores del rendimiento de un proceso en ejecución. En este contexto, este trabajo define una arquitectura para el proceso de predicción de indicadores que, asimismo, contempla la posibilidad de la actualización del modelo predictivo a lo largo del tiempo.

Autores: Alfonso E. Márquez-Chamorro / Manuel Resinas, / Antonio Ruiz-Cortés / 
Palabras Clave: minería de procesos - monitorización predictiva - predicción de indicadores. - Procesos de Negocio

3 -

4 - Run-time prediction of business process indicators using evolutionary decision rules (Summary)

Summary of the contribution

Predictive monitoring of business processes is a challenging topic of process min- ing which is concerned with the prediction of process indicators of running pro- cess instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for pre- dictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process man- agement systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).

Autores: Alfonso E. Márquez-Chamorro / Manuel Resinas / Antonio Ruiz-Cortés / 
Palabras Clave: Business process indicator - Business Process Management - Evolutionary algorithm - Predictive mon- itoring - Process Mining