TY - GEN
T1 - Analyzing process concept drifts based on sensor event streams during runtime
AU - Stertz, Florian
AU - Rinderle-Ma, Stefanie
AU - Mangler, Juergen
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Business processes have to adapt to constantly changing requirements at a large scale due to, e.g., new regulations, and at a smaller scale due to, e.g., deviations in sensor event streams such as warehouse temperature in manufacturing or blood pressure in health care. Deviations in the process behavior during runtime can be detected from process event streams as so called concept drifts. Existing work has focused on concept drift detection so far, but has neglected why the drift occurred. To close this gap, this paper provides online algorithms to analyze the root cause for a concept drift using sensor event streams. These streams are typically gathered externally, i.e., separated from the process execution, and can be understood as time sequences. Supporting domain experts in assessing concept drifts through their root cause facilitates process optimization and evolution. The feasibility of the algorithms is shown based on a prototypical implementation. Moreover, the algorithms are evaluated based on a real-world data set from manufacturing.
AB - Business processes have to adapt to constantly changing requirements at a large scale due to, e.g., new regulations, and at a smaller scale due to, e.g., deviations in sensor event streams such as warehouse temperature in manufacturing or blood pressure in health care. Deviations in the process behavior during runtime can be detected from process event streams as so called concept drifts. Existing work has focused on concept drift detection so far, but has neglected why the drift occurred. To close this gap, this paper provides online algorithms to analyze the root cause for a concept drift using sensor event streams. These streams are typically gathered externally, i.e., separated from the process execution, and can be understood as time sequences. Supporting domain experts in assessing concept drifts through their root cause facilitates process optimization and evolution. The feasibility of the algorithms is shown based on a prototypical implementation. Moreover, the algorithms are evaluated based on a real-world data set from manufacturing.
KW - Concept drift
KW - Dynamic Time Warping
KW - Online process mining
KW - Root cause analysis
KW - Sensor event stream
KW - Time sequence
UR - http://www.scopus.com/inward/record.url?scp=85091283596&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58666-9_12
DO - 10.1007/978-3-030-58666-9_12
M3 - Conference contribution
AN - SCOPUS:85091283596
SN - 9783030586652
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 202
EP - 219
BT - Business Process Management - 18th International Conference, BPM 2020, Proceedings
A2 - Fahland, Dirk
A2 - Ghidini, Chiara
A2 - Becker, Jörg
A2 - Dumas, Marlon
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Business Process Management, BPM 2020
Y2 - 13 September 2020 through 18 September 2020
ER -