Abstract | ||
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Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods. |
Year | DOI | Venue |
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2017 | 10.1007/978-3-319-59536-8_30 | ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017) |
DocType | Volume | ISSN |
Conference | 10253 | 0302-9743 |
Citations | PageRank | References |
11 | 0.57 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Niek Tax | 1 | 83 | 12.43 |
Ilya Verenich | 2 | 33 | 2.68 |
marcello la rosa | 3 | 1402 | 81.70 |
Marlon Dumas | 4 | 5742 | 371.10 |