Autor:
Márquez Chamorro, Alfonso E.

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E-mails conocidos

alfedu@gmail.com
amarquez6@us.es

Fecha de nacimiento

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Puesto de trabajo

Apellidos

Márquez Chamorro

Nombre de pila

Alfonso E.

Nombre

Nombres alternativos

Marquez Chamorro, Alfonso
Márquez-Chamorro, Alfonso E.

Afiliaciones conocidas

University of Seville, Spain
Pablo de Olavide University, Spain
Dpto. Lenguajes y Sistemas Informáticos, University of Seville, Seville, Spain.
Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla
Universidad de Sevilla, Spain

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2016 - 20192
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Tema: Business Process ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Artículo
    Towards a general architecture for predictive monitoring of business processes
    Márquez Chamorro, Alfonso E.; Resinas, Manuel; Ruiz-Cortés, Antonio. Actas de las XII Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2016), 2016-07-13.
    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.
  • Miniatura
    Artículo
    A Hybrid Reliability Metric for SLA Predictive Monitoring
    Comuzzi, Marco; Márquez Chamorro, Alfonso E.; Resinas, Manuel. Actas de las XV Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2019), 2019-09-02.
    Modern SLA management includes SLA prediction based on data collected during service operations. Besides overall accuracy of a prediction model, decision makers should be able to measure the reliability of individual predictions before taking important decisions, such as whether to renegotiate an SLA. Measures of reliability of individual predictions provided by machine learning techniques tend to depend strictly on the technique chosen and to neglect the features of the system generating the data used to learn a model, i.e., the service provisioning landscape in this case. In this paper, we define a hybrid measure of reliability of an individual SLA prediction for classification models, which accounts for both the reliability of the chosen prediction technique, if available, and features capturing the variability of the service provisioning scenario. The metric is evaluated empirically using SLAs and event logs of a real world case. This paper was presented in ACM Symposium on Applied Computing (SAC) in April 2019 (GGS Class 2).