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• W2767415237 abstract "Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal." @default.
• W2767415237 created "2017-11-17" @default.
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• W2767415237 date "2017-11-10" @default.
• W2767415237 modified "2023-10-13" @default.
• W2767415237 title "LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances" @default.
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• W2767415237 hasPublicationYear "2017" @default.
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