Predictive monitoring of business processes aims at predicting the future of an ongoing process execution. In this work, we focus on the prediction of the next activities to be executed in a running case. However, in contrast with most state-of-The-Art approaches, focused on predicting exactly the next activity that will be executed from the current state of the process, we propose an approach aimed at predicting the portion of the process (or 'location') that is likely to be executed next. The notion of location allows us to detect activities belonging to the same portion of a control-flow construct (e.g., at the beginning of a parallelism, or at the end of a loop). It provides an abstraction mechanism from the level of the single activity, which can be used to provide the process analyst with an higher-level overview of what can be expected next in the process execution. We validated the approach over a set of real-world datasets comparing and discussing different strategies for training a classifier in returning a location in place of an activity label.

Towards next-location prediction for process executions

Chiorrini A.;Diamantini C.;Pioli M.;Potena D.
2022-01-01

Abstract

Predictive monitoring of business processes aims at predicting the future of an ongoing process execution. In this work, we focus on the prediction of the next activities to be executed in a running case. However, in contrast with most state-of-The-Art approaches, focused on predicting exactly the next activity that will be executed from the current state of the process, we propose an approach aimed at predicting the portion of the process (or 'location') that is likely to be executed next. The notion of location allows us to detect activities belonging to the same portion of a control-flow construct (e.g., at the beginning of a parallelism, or at the end of a loop). It provides an abstraction mechanism from the level of the single activity, which can be used to provide the process analyst with an higher-level overview of what can be expected next in the process execution. We validated the approach over a set of real-world datasets comparing and discussing different strategies for training a classifier in returning a location in place of an activity label.
979-8-3503-9714-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/309923
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