Predicting process behaviour using deep learning

By Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke. 2016

407 citations recorded by the source corpus.

Abstract Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

Subfields: Information Systems, Management Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Mathias Kraus
2. Alexander M. Rush
3. Sebastian Gehrmann
4. Stefan Feuerriegel

In Management Information Systems

1. Mathias Kraus
2. Alexander M. Rush
3. Stefan Feuerriegel

In Management Science and Operations Research

1. Mathias Kraus
2. Hanspeter Pfister
3. Alexander M. Rush
4. Sebastian Gehrmann
5. Stefan Feuerriegel
6. Longbing Cao
7. Dogu Araci
8. Hinrich Schütze

Citations in the corpus

• Decision support from financial disclosures with deep neural networks and transfer learning (2017, background): “For instance, in the related field of process mining, LSTMs have proven capable of effectively learning long sequences [18].”
• LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks (2016, positive): “[8] describe the application of LSTMVis to understand a trained model for a business process intelligence dataset.”