Predicting process behaviour using deep learning
Joerg Evermann,Jana-Rebecca Rehse,Jana-Rebecca Rehse,Peter Fettke,Peter Fettke +4 more
- 01 Aug 2017
- Vol. 100, pp 129-140
TL;DR: This paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process, and shows results that surpass the state-of-the-art in prediction precision.
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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.
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Figures
Table 1: RNN Parameters 
Table 7: Validation precision for different number of unrolled steps 
Figure 10: LSTM visualization of states for declined loan application processes 
Figure 1: RNN architecture with single hidden layer of LSTM cells, unrolled five steps 
Figure 6: Training precision by training epoch for selected datasets (mean over 10 training folds) 
Table 3: Results and comparison to (Breuker et al., 2016)
Citations
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
TL;DR: This work presents LSTMVis, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics, and describes the domain, the different stakeholders, and their goals and tasks.
430
Deep learning for affective computing: Text-based emotion recognition in decision support
Bernhard Kratzwald,Suzana Ilic,Mathias Kraus,Stefan Feuerriegel,Helmut Prendinger +4 more
- 01 Nov 2018
TL;DR: This work proposes sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition.
Process mining techniques and applications – A systematic mapping study
Cleiton dos Santos Garcia,Alex Meincheim,Elio Ribeiro Faria Junior,Marcelo Rosano Dallagassa,Denise Maria Vecino Sato,Denise Maria Vecino Sato,Deborah Ribeiro Carvalho,Eduardo Alves Portela Santos,Edson Emílio Scalabrin +8 more
TL;DR: It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements, and finally application domains among different business segments are reported on.
323
Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud
Yibo Wang,Wei Xu +1 more
- 01 Jan 2018
TL;DR: A novel deep learning model for automobile insurance fraud detection that uses Latent Dirichlet Allocation (LDA)-based text analytics that outperforms widely used machine learning models, such as random forests and support vector machine.
315
Decision support from financial disclosures with deep neural networks and transfer learning
Mathias Kraus,Stefan Feuerriegel +1 more
- 01 Dec 2017
TL;DR: The use of deep neural networks for financial decision support is studied and a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures is revealed.
302
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