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Predicting Activities in Business
Processes with LSTM Recurrent
Neural Networks
26-28 November
Santa Fe, Argentina
Edgar Tello-Leal, Jorge Roa, Mariano Rubiolo, Ulises Ramirez
CIDISI
Santa Fe Regional Faculty, National Technological University
26-28 November
Santa Fe, Argentina
Agenda
• Introduction
• Goal
• Process Mining and LSTM Neural Networks
• Approach
• Results
• Conclusion and Future Work
26-28 November
Santa Fe, Argentina
Introduction
5G
Predicting the behavior of a business process, i.e.
exploiting event logs to make predictions about the
execution of activities, is a key aspect to provide
valuable input for planning and resource allocation.
IoT
Industry 4.0
BPM
SOA
IoS
EventLogs
Diagnosis, performance indicators, traceability
26-28 November
Santa Fe, Argentina
Introduction
EventLogs
Process
mining
techniquesLSTM
Neural
Networks*
Predicting
the behavior
of a business
process
5G
IoT
Industry 4.0
BPM
SOA
* N. Tax, I. Verenich, M. La Rosa, and M. Dumas, “Predictive business process monitoring with LSTM neural networks,” in Advanced Information SystemsEngineering, E. Dubois and K. Pohl, Eds. Cham: Springer International Publishing, 2017, pp. 477–492.
IoS
26-28 November
Santa Fe, Argentina
Goal
Approach for the discovery of events and activities of a
business process through predictive analysis from
traces contained in event logs taken from the IoT and
information systems in the Industry 4.0 domain.
EventLogs
LSTM
Predictive model
26-28 November
Santa Fe, Argentina
Process Mining*
* Process Mining: Data Science in Action by W.M.P. van der Aalst, Springer Verlag, 2016 (ISBN 978-3-662-49850-7)
Discovery
Conformance
Enhancement
Process model
Record events
ERP, MES, etc
BottlenecksFocused analysis of:• Bottlenecks• Breakdowns• Rework• Rejected parts• Long idle times• Comparison of workers
performanceEventLog
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Santa Fe, Argentina
Long Short-Term Memory Neural Network*
* H. Sak, A.W. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acousticmodeling,” in 15th Annual Conference of the International Speech Communication Association, September 2014, pp. 338–342.
• The Long Short-Term Memory (LSTM) neural network is an extension of the Recurrent Neural Network (RNN).
• It has excellent performance for sequential problems.
• Two types of input:– The present.
– The recent past.
• RNN use both types of input to determine how they behave with respect to new data:– The output of a RNN at time step t-1 affects its
output at time step t.
medium.com
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Santa Fe, Argentina
Approach
26-28 November
Santa Fe, Argentina
Case Study: Event Log
* Available: https://data.4tu.nl/repository/collection:event_logs
• Cases: 255• Activities: 56• Traces: 4541
LSTM
Input activity
Output activity
26-28 November
Santa Fe, Argentina
Case Study: Preliminary Results
*Code and dataset available at: http://dx.doi.org/10.17632/trskzyg3j9.1
26-28 November
Santa Fe, Argentina
Conclusion
• An approach for the prediction of business process activities has been
proposed.– Based on an LSTM recurrent neural network.
– Exploit event logs to make predictions about the execution of cases.
– Key to provide valuable input for planning and resource allocation (either physical or virtual).
• To show the applicability to the proposed domain we present preliminary
results based on a dataset with 255 traces.
• The predictive analysis implemented in this work gave us useful information
to determine the next activity of a sequence of activities based on event
logs.
26-28 November
Santa Fe, Argentina
Future Work
• Multi-task learning approach to predict other attributes of the next activity.
• The proposed technique can be extended to other real-life event logs.– Cloud computing.
– Predictive maintenance.
– BPI Challenge 2018 or Hospital billing.
Thank you !!
Edgar Tello-Leal ([email protected])Jorge Roa ([email protected])
Mariano Rubiolo ([email protected])Ulises M. Ramirez-Alcocer ([email protected])
