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Context-Aware Process Performance Indicator Prediction

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phaseguides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.This paper was published in IEEE Access, 2020, Vol. 8, pp. 222050 – 222063, doi: 10.1109/ACCESS.2020.3044670

Autores: Alfonso E. Márquez-Chamorro / Kate Revoredo / Manuel Resinas / Adela Del-Río-Ortega / Flavia Santoro / Antonio Ruiz-Cortés / 
Palabras Clave: Business Process Management - context-awareness - predictive monitoring - process indicator prediction - Process Mining

A Hybrid Reliability Metric for SLA Predictive Monitoring

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).

Autores: Marco Comuzzi / Alfonso Marquez Chamorro / Manuel Resinas / 
Palabras Clave: Business process - predictive monitoring - Reliability - SLA monitoring

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