TY - GEN
T1 - Queueing inference for process performance analysis with missing life-cycle data
AU - Berkenstadt, Guy
AU - Gal, Avigdor
AU - Senderovich, Arik
AU - Shraga, Roee
AU - Weidlich, Matthias
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Measuring key performance indicators, such as queue lengths and waiting times, using event logs serve for improvement of resource-driven business processes. However, existing techniques assume the availability of complete life cycle information, including the time a case was scheduled for execution (aka arrival times). Yet, in practice, such information may be missing for a large portion of the recorded cases. In this paper, we propose a methodology to address missing life-cycle data by incorporating predicted information in business processes performance analysis. Our approach builds upon techniques from queueing theory and leverages supervised learning to accurately predict performance indicators based on an event log with missing data. Our experimental results using both synthetic and real-world data demonstrate the effectiveness of our approach.
AB - Measuring key performance indicators, such as queue lengths and waiting times, using event logs serve for improvement of resource-driven business processes. However, existing techniques assume the availability of complete life cycle information, including the time a case was scheduled for execution (aka arrival times). Yet, in practice, such information may be missing for a large portion of the recorded cases. In this paper, we propose a methodology to address missing life-cycle data by incorporating predicted information in business processes performance analysis. Our approach builds upon techniques from queueing theory and leverages supervised learning to accurately predict performance indicators based on an event log with missing data. Our experimental results using both synthetic and real-world data demonstrate the effectiveness of our approach.
KW - Process Mining, Queueing Inference Engine, Performance Analysis, Supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85096528648&partnerID=8YFLogxK
U2 - 10.1109/ICPM49681.2020.00019
DO - 10.1109/ICPM49681.2020.00019
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AN - SCOPUS:85096528648
T3 - Proceedings - 2020 2nd International Conference on Process Mining, ICPM 2020
SP - 57
EP - 64
BT - Proceedings - 2020 2nd International Conference on Process Mining, ICPM 2020
A2 - van Dongen, Boudewijn
A2 - Montali, Marco
A2 - Wynn, Moe Thandar
T2 - 2nd International Conference on Process Mining, ICPM 2020
Y2 - 4 October 2020 through 9 October 2020
ER -