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Predictive Business Process
Monitoring with LSTM Neural
Networks
Niek Tax (TU/e)
Ilya Verenich (QUT)
Marcello La Rosa (QUT...
Predictive Business Process Monitoring
in a Nutshell
PAGE 1
Predictive Monitoring: General Approach
PAGE 2
Predictive Monitoring Example
PAGE 3
Current
situation
• What is the next activity for
this case?
• When is this next acti...
Recurrent Neural Networks
PAGE 4
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436-444 (2015)
Has pr...
Long Short-Term Memory
PAGE 5Figure from http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Hochreiter, S., Schmidh...
From Event to Feature Vector
PAGE 6
One-hot encoding
1) Time since midnight
2) Time since week start
3) Time since last ev...
Neural Network Architectures
PAGE 7
Data Sets for Evaluation
• Helpdesk log
- Ticketing management
process of the helpdesk of an
Italian software company
• BP...
Baseline Technique for Time Prediction
PAGE 9
van der Aalst, W.M.P., Schonenberg, M.H., & Song, M. (2011). Time
prediction...
Predicting the Next Activity and its Time
PAGE 10
2-layers of which
one layer shared
is the best
architecture on
both data...
Predicting the Suffix of a Case
PAGE 11
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Time and activity sequence p...
Predicting the Remaining Cycle Time
PAGE 12
Conclusions
• LSTMs can be used as a general framework for prediction tasks in
the context of business processes that outp...
Neural Network Details
• Learning algorithm: Adam
• Loss Functions
• Batch Normalization layer after every two layers
PAGE...
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Predictive Business Process Monitoring with LSTM Neural Networks

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Research paper presentation on deep learning for predictive monitoring of business processes. Talk delivered by Niek Tax at the CAiSE'2017 conference, 15 June 2017. Research paper available at: http://tinyurl.com/yambgtng and source code available at: https://github.com/verenich/ProcessSequencePrediction/

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Predictive Business Process Monitoring with LSTM Neural Networks

  1. 1. Predictive Business Process Monitoring with LSTM Neural Networks Niek Tax (TU/e) Ilya Verenich (QUT) Marcello La Rosa (QUT) Marlon Dumas (Tartu) June 15th, 2017
  2. 2. Predictive Business Process Monitoring in a Nutshell PAGE 1
  3. 3. Predictive Monitoring: General Approach PAGE 2
  4. 4. Predictive Monitoring Example PAGE 3 Current situation • What is the next activity for this case? • When is this next activity going to take place? • How long is this case still going to take until it is finished? • What is the outcome of this case? Is the compensation going to be paid? Or rejected?
  5. 5. Recurrent Neural Networks PAGE 4 LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436-444 (2015) Has problems with long-term dependencies in the data!
  6. 6. Long Short-Term Memory PAGE 5Figure from http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735-1780 (1997) How do we represent events as neural network inputs?
  7. 7. From Event to Feature Vector PAGE 6 One-hot encoding 1) Time since midnight 2) Time since week start 3) Time since last event <1,0,0,0,0,0,0,0> • 8 different activities • Each position represents one activity
  8. 8. Neural Network Architectures PAGE 7
  9. 9. Data Sets for Evaluation • Helpdesk log - Ticketing management process of the helpdesk of an Italian software company • BPI Challenge 2012 W subprocess log - A financial loan application process at a large financial institution in the Netherlands PAGE 8 Events 13 710 Cases 3 804 Activities 9 • Environmental Permit log - Process of handling environmental permit applications at a Dutch municipality Events 72 413 Cases 9 658 Activities 6 Events 38 944 Cases 937 Activities 381
  10. 10. Baseline Technique for Time Prediction PAGE 9 van der Aalst, W.M.P., Schonenberg, M.H., & Song, M. (2011). Time prediction based on process mining. Information Systems, 36(2), 450-475.
  11. 11. Predicting the Next Activity and its Time PAGE 10 2-layers of which one layer shared is the best architecture on both data sets LSTMs outperform traditional RNNs LSTMs outperform the transition system based approach for time-of- next-event prediction LSTMs are more accurate in predicting the next activity on BPI’12 W than 2 baseline methods
  12. 12. Predicting the Suffix of a Case PAGE 11 Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Time and activity sequence prediction of business process instances. arXiv preprint arXiv:1602.07566 (2016)
  13. 13. Predicting the Remaining Cycle Time PAGE 12
  14. 14. Conclusions • LSTMs can be used as a general framework for prediction tasks in the context of business processes that outperforms tailor-made approaches on a range of tasks and data sets • Predicting time and activity in one shared model outperforms predicting both in separate models • We identified a limitation of the technique when event logs contain many repeated events • Code and documentation is available at: http://verenich.github.io/ProcessSequencePrediction PAGE 13
  15. 15. Neural Network Details • Learning algorithm: Adam • Loss Functions • Batch Normalization layer after every two layers PAGE 14 Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning (pp. 448-456). Kingma, D., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference for Learning Representations Objective Loss Function Activity Cross Entropy Loss Time Mean Absolute Error Loss

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