Predictive business process monitoring aims to predict how an ongoing process instance will unfold up to its completion, thereby facilitating proactively responding to anticipated problems. Recurrent Neural Networks (RNNs), a special form of deep learning techniques, gain interest as a prediction technique in BPM. However, non-sequential control flows may make the prediction task more difficult, because RNNs were conceived for learning and predicting sequences of data. Based on an industrial dataset, we provide experimental results comparing different alternatives for considering non- sequential control flows. In particular, we consider cycles and parallelism for business process prediction with RNNs.

1. Considering Non-sequential
Control Flows for Process
Prediction with Recurrent
Neural Networks
Andreas Metzger, Adrian Neubauer

2. Predictive Process Monitoring
2SEAA 2018, Prague
Monitoring
Prediction
Decision
Time
t t + 
Acceptable/
Planned
Situations
Violation
Proactive
Adaptation
No
Violation

Prediction accuracy is essential
• Missing a true violation  No adaptation  Violation not prevented
• Predicting a false violation  Unnecessary adaptation
e.g., completion of transport
process by given deadline

3. Process Prediction with RNNs
RNN = Recurrent Neural Network
• Special type of artificial neural network
• Neuron feeds back information into itself
Advantages of RNNs
• High accuracy in general
• Can handle arbitrary number of process steps
• One prediction model sufficient to make prediction at any point in time
(“checkpoint”)
Problem
• RNNs devised for natural language processing (linear sequences of text)
• Non-sequential control flow (order of process steps) may make
RNN prediction more difficult
 How to consider non-sequential control flows?
3

4. Considering Non-sequential Control Flows
Cycles
• Incremental prediction
• Direct prediction
Parallel branches
• No path: No encoding of parallel branches
• Path: Parallel branch encoded as
attribute of process step
• Slice: Encoding of steps running in parallel
4
No path A E F
Path A1 E2 . F2
Slice AE BE BF
Parallel
Branch 1
No path:
Path:
Slice:
A B
E F
Parallel
Branch 2
Accumulation of prediction error
B A A A

5. Experiment
Cargo 2000 Data Set
• 3,942 process instances
• 56,082 process steps
• Challenging: Parallel branches include
same types of process steps
Environment / Tooling
• Adaptation of LSTM-Realization of RNN [Tax et al. @ CAiSE 2017]
• Cloud environment for training: 15 physical machines, dockerized
• Ca. 3 hours training / model
Accuracy Metric: MCC
• Robust against class imbalances
• More challenging to score high on
5

6. -0,05
0,05
0,15
0,25
0,35
0,45
0,55
0,65
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Results
SEAA 2018, Prague 6
Incremental Prediction Direct Prediction
-0,05
0,05
0,15
0,25
0,35
0,45
0,55
0,65
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
MCC
No path Path Slice No path Path Slice
Accuracy [MCC]
Checkpoint
MLP
Direct Prediction 6.3% (avg) better than Incremental
But: Flip after 50% mark!
0,24750
-36%
RNN 36% better than “traditional” Neural Network
Impact of parallel encoding very small: 1.4% (avg)

7. Conclusion and Outlook
• Deep learning promising technique for predictive business
process monitoring
• Facilitates proactive business process management based on
accurate predictions
• Improvement of accuracy when explicitly considering non-
sequential control flows
• Cycles: +6.3 %
• Parallel branches: +1.4%
• Future work
• Further empirical evidence (port logistics, e-commerce, …)
• Measure impact of control flow structure and complexity on
accuracy
SEAA 2018, Prague 7

8. Thank you!
…the EFRE co-financed operational
program NRW.Ziel2
http://www.lofip.de
…the EU’s Horizon 2020 research and innovation
programme under Objective ICT-15 ‘Big Data PPP: Large
Scale Pilot Actions ‘
http://www.transformingtransport.eu
Research leading to these results has received funding
from…
SEAA 2018, Prague 8