Predicting Process Activities and Timestamps with Entity-Embeddings Neural Networks
| Autor: | Daniel Cortinovis, Benjamin Dalmas, Fabrice Baranski |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Research Challenges in Information Science ISBN: 9783030750176 RCIS |
| Popis: | Predictive process monitoring aims at predicting the evolution of running traces based on models extracted from historical event logs. Standard process prediction techniques are limited to the prediction of the next activity in a running trace. As a consequence, processes with complex topology (i.e. with several events having similar start/end time) are impossible to predict with these classical multinomial classification approaches. In this paper, the goal is to exploit an original features engineering technique which converts the historical event log of a process into different topological and temporal features, capturing the behavior and context of execution of previous events. These features are then used to train an Entity Embeddings Neural Network in order to learn a model able to predict, in a one-shot manner, both the remaining activities until the end in a running trace and the associated timestamp. Experiments show that this approach globally outperforms previous work for both types of predictions. |
| Databáze: | OpenAIRE |
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