TY - GEN
T1 - Next-Activity Prediction for Non-stationary Processes with Unseen Data Variability
AU - Mangat, Amolkirat Singh
AU - Rinderle-Ma, Stefanie
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Predictive Process Monitoring (PPM) enables organizations to predict future states of ongoing process instances such as the remaining time, the outcome, or the next activity. A process in this context represents a coordinated set of activities that are enacted by a process engine in a specific order. The underlying source of data for PPM are event logs (ex post) or event streams (runtime) emitted for each activity. Although plenty of methods have been proposed to leverage event logs/streams to build prediction models, most works focus on stationary processes, i.e., the methods assume the range of data variability encountered in the event log/stream to remain the same over time. Unfortunately, this is not always the case as deviations from the expected process behaviour might occur quite frequently and updating prediction models becomes inevitable eventually. In this paper we investigate non-stationary processes, i.e., the impact of unseen data variability in event streams on prediction models from a structural and behavioural point of view. Strategies and methods are proposed to incorporate unknown data variability and to update recurrent neural network based models continuously in order to accommodate changing process behaviour. The approach is prototypically implemented and evaluated based on real-world data sets.
AB - Predictive Process Monitoring (PPM) enables organizations to predict future states of ongoing process instances such as the remaining time, the outcome, or the next activity. A process in this context represents a coordinated set of activities that are enacted by a process engine in a specific order. The underlying source of data for PPM are event logs (ex post) or event streams (runtime) emitted for each activity. Although plenty of methods have been proposed to leverage event logs/streams to build prediction models, most works focus on stationary processes, i.e., the methods assume the range of data variability encountered in the event log/stream to remain the same over time. Unfortunately, this is not always the case as deviations from the expected process behaviour might occur quite frequently and updating prediction models becomes inevitable eventually. In this paper we investigate non-stationary processes, i.e., the impact of unseen data variability in event streams on prediction models from a structural and behavioural point of view. Strategies and methods are proposed to incorporate unknown data variability and to update recurrent neural network based models continuously in order to accommodate changing process behaviour. The approach is prototypically implemented and evaluated based on real-world data sets.
KW - Data variability
KW - Event streams
KW - Non-stationary prediction
KW - Predictive process monitoring
UR - http://www.scopus.com/inward/record.url?scp=85140483538&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-17604-3_9
DO - 10.1007/978-3-031-17604-3_9
M3 - Conference contribution
AN - SCOPUS:85140483538
SN - 9783031176036
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 161
BT - Enterprise Design, Operations, and Computing - 26th International Conference, EDOC 2022, Proceedings
A2 - Almeida, João Paulo A.
A2 - Karastoyanova, Dimka
A2 - Guizzardi, Giancarlo
A2 - Fonseca, Claudenir M.
A2 - Montali, Marco
A2 - Maggi, Fabrizio Maria
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Enterprise Design, Operations, and Computing, EDOC 2022
Y2 - 3 October 2022 through 7 October 2022
ER -