TY - GEN
T1 - Change point detection and dealing with gradual and multi-order dynamics in process mining
AU - Martjushev, J.
AU - Jagadeesh Chandra Bose, R.P.
AU - van der Aalst, W.M.P.
PY - 2015
Y1 - 2015
N2 - In recent years process mining techniques have matured. Provided that the process is stable and enough example traces have been recorded in the event log, it is possible to discover a high-quality process model that can be used for performance analysis, compliance checking, and prediction. Unfortunately, most processes are not in steady-state and process discovery techniques have problems uncovering "second-order dynamics" (i.e., the process itself changes while being analyzed). This paper describes an approach to discover a variety of concept drifts in processes. Unlike earlier approaches, we can discover gradual drifts and multi-order dynamics (e.g., recurring seasonal effects mixed with the effects of an economic crisis). We use a novel adaptive windowing approach to robustly localize changes (gradual or sudden). Our extensive evaluation (based on objective criteria) shows that the new approach is able to efficiently uncover a broad range of drifts in processes.
AB - In recent years process mining techniques have matured. Provided that the process is stable and enough example traces have been recorded in the event log, it is possible to discover a high-quality process model that can be used for performance analysis, compliance checking, and prediction. Unfortunately, most processes are not in steady-state and process discovery techniques have problems uncovering "second-order dynamics" (i.e., the process itself changes while being analyzed). This paper describes an approach to discover a variety of concept drifts in processes. Unlike earlier approaches, we can discover gradual drifts and multi-order dynamics (e.g., recurring seasonal effects mixed with the effects of an economic crisis). We use a novel adaptive windowing approach to robustly localize changes (gradual or sudden). Our extensive evaluation (based on objective criteria) shows that the new approach is able to efficiently uncover a broad range of drifts in processes.
U2 - 10.1007/978-3-319-21915-8_11
DO - 10.1007/978-3-319-21915-8_11
M3 - Conference contribution
SN - 978-3-319-21914-1
T3 - Lecture Notes in Business Information Processing
SP - 161
EP - 178
BT - Perspectives in Business Informatics Research
A2 - Matulevicius, R.
A2 - Dumas, M.
PB - Springer
CY - Dordrecht
ER -