Learning in the presence of concept drift and hidden contexts
Machine Learning
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
YAWL: yet another workflow language
Information Systems
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Abstractions in Process Mining: A Taxonomy of Patterns
BPM '09 Proceedings of the 7th International Conference on Business Process Management
CPN tools for editing, simulating, and analysing coloured Petri nets
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Change patterns and change support features in process-aware information systems
CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift
ACM SIGKDD Explorations Newsletter
Process Mining: Overview and Opportunities
ACM Transactions on Management Information Systems (TMIS)
Distributed process discovery and conformance checking
FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
What makes a good process model?
Software and Systems Modeling (SoSyM)
Online techniques for dealing with concept drift in process mining
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Information Sciences: an International Journal
Retrieval and clustering for supporting business process adjustment and analysis
Information Systems
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Operational processes need to change to adapt to changing circumstances, e.g., new legislation, extreme variations in supply and demand, seasonal effects, etc.While the topic of flexibility is well-researched in the BPM domain, contemporary process mining approaches assume the process to be in steady state. When discovering a process model from event logs, it is assumed that the process at the beginning of the recorded period is the same as the process at the end of the recorded period. Obviously, this is often not the case due to the phenomenon known as concept drift. While cases are being handled, the process itself may be changing. This paper presents an approach to analyze such second-order dynamics. The approach has been implemented in ProM1 and evaluated by analyzing an evolving process.