An Eye into the Future: Leveraging A-priori Knowledge in Predictive Business Process Monitoring

Francescomarino, Chiara Di; Ghidini, Chiara; Maggi, Fabrizio Maria; Petrucci, Giulio; Yeshchenko, Anton

doi:10.1007/978-3-319-65000-5_15

Cited by 77 publications

(69 citation statements)

References 25 publications

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“…Concerning the Deep Learning approach, there have been several studies that explore the usage of Deep Neural Network for predictive process monitoring (cf. [63,26,27,23,40]). However, they focus on predicting the name of the future activities/events, the next timestamp, and the remaining processing time.…”

Section: Discussion On the Experimentsmentioning

confidence: 99%

“…Another major stream of works tackle the problem of predicting the future activities/events of a running process (cf. [63,26,27,23,40,15,53]). The works by [63,26,27,23,40] use deep learning approach for predicting the future events, e.g., the next event of the current running process.…”

Section: Related Workmentioning

confidence: 99%

“…The works by [63,26,27,23,40] use deep learning approach for predicting the future events, e.g., the next event of the current running process. Specifically, [63,26,27,23] use LSTM neural network, while [40] uses deep feed-forward neural network. In [53,23,63] the authors also tackle the problem of predicting the whole sequence of future events (the suffix of the current running process).…”

Section: Related Workmentioning

confidence: 99%

“…Through predictive analysis, potential future problems can be detected and preventive actions can be taken in order to avoid unexpected situation, e.g., processing delay and Service-Level Agreement (SLA) violations. Many studies have been conducted in order to deal with various prediction tasks such as predicting the remaining processing time [4,63,54,52,53], predicting the outcomes of a process [37,22,67,50], predicting future events [23,63,27], etc (cf. [43,42,58,49,15,19]).…”

Section: Introductionmentioning

confidence: 99%

“…Though there have been some works that exhibit the usage of deep learning models in predictive process monitoring (cf. [63,26,27,23,40]), here we consider the prediction tasks that are different from the tasks that have been studied in those works. We also add more thorough explanation on several concepts and ideas of our approach so as to provide a better understanding.…”

Section: Introductionmentioning

confidence: 99%

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Specification-driven predictive business process monitoring

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Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.

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