Article
Context-Aware Process Performance Indicator Prediction
Author/s | Márquez Chamorro, Alfonso Eduardo
Revoredo, Kate Resinas Arias de Reyna, Manuel Río Ortega, Adela del Santoro, Flavia María Ruiz Cortés, Antonio |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2020 |
Deposit Date | 2022-03-17 |
Published in |
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Abstract | 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 ... 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 phase guides 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. |
Funding agencies | European Union (UE). H2020 Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Project ID. | No. 645751 (RISE BPM)
HORATIO (RTI2018-101204-B-C21) OPHELIA RTI2018-101204-B-C22 |
Citation | Márquez Chamorro, A.E., Revoredo, K., Resinas Arias de Reyna, M., Río Ortega, A.d., Santoro, F.M. y Ruiz Cortés, A. (2020). Context-Aware Process Performance Indicator Prediction. IEEE Access, 8, 222050-222063. |
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