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 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.
Highlights
IntroductionProcess mining [1] allows the extraction of useful information from event logs and historical data of business processes
Process mining [1] allows the extraction of useful information from event logs and historical data of business processes.This information can be used to improve the performance of these business processes
If we want to predict the state of an 98 activity, the involved human resource can be the context to be considered while for predicting the remaining time of a process execution the priority variable may be the context to consider. We address this issue by identifying the context information of a business process related to a cer- tain indicator so that it can be used to improve its prediction
Summary
Process mining [1] allows the extraction of useful information from event logs and historical data of business processes. This information can be used to improve the performance of these business processes. Some examples are the remaining execution time of a process instance, the likelihood of a fault in the system or the abnormal termination of a running instance These predictions enable the application of proactive and corrective actions to improve process performance and mitigate possible risks in real time. This introductory section provides some background on the context identification in BPM and the predictive monitoring of business processes. According to [22], context is an open concept, since it is not limited to the imagination of a person, while [23] explains context as "any information that can be used to characterize the situation of an entity." Yet,
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