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- W4313402643 abstract "Event logs are a collection of events that contain the execution of a business process. In them, each event is considered an observation and describe the feature of its execution. These events are regrouped into instance or case. Ina given instance, we can find that some events have not been executed properly and therefore, there is usually a feature that describes the status of the event. This type of execution is considered as failure and many publications usually propose prediction model to improve of the business model. In this work, we propose a prediction model for the failure of a business process event while considering the loops as failure. The loop can be considered as a repeated execution on an event during an instance. For the proposed model, we use two deep learning algorithms and then compared the best one. And finally, since, while using deep learning, it is hard to determine which feature are important for the prediction model, we have given an explainability of the model. For this work, we use the event log’s dataset of a loan application done in a financial institution." @default.
- W4313402643 created "2023-01-06" @default.
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- W4313402643 date "2023-01-01" @default.
- W4313402643 modified "2023-09-27" @default.
- W4313402643 title "Prediction of the Failure of a Business Process Event Using Event Log and Deep Learning" @default.
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- W4313402643 doi "https://doi.org/10.1007/978-3-031-21435-6_23" @default.
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