Automating process discovery through event-data analysis
Proceedings of the 17th international conference on Software engineering
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Business process mining: An industrial application
Information Systems
A Design Science Research Methodology for Information Systems Research
Journal of Management Information Systems
Managing and Mining Graph Data
Managing and Mining Graph Data
Process mining framework for software processes
ICSP'07 Proceedings of the 2007 international conference on Software process
What makes process models understandable?
BPM'07 Proceedings of the 5th international conference on Business process management
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Design science in information systems research
MIS Quarterly
A generic import framework for process event logs
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
The prom framework: a new era in process mining tool support
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
Design science research evaluation
DESRIST'12 Proceedings of the 7th international conference on Design Science Research in Information Systems: advances in theory and practice
A comprehensive framework for evaluation in design science research
DESRIST'12 Proceedings of the 7th international conference on Design Science Research in Information Systems: advances in theory and practice
Where did i misbehave? diagnostic information in compliance checking
BPM'12 Proceedings of the 10th international conference on Business Process Management
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Process mining has been gaining significant attention in academia and practice. A promising first step to apply process mining in the audit domain was taken with the mining of process instances from accounting data. However, the resulting process instances constitute graphs. Commonly, timestamp oriented event log formats require a sequential list of activities and do not support graph structures. Thus, event log based process mining techniques cannot readily be applied to accounting data. To close this gap, we present an algorithm that determines an activity sequence from accounting data. With this algorithm, mined process instance graphs can be decomposed in a way they fit into sequential event log formats. Event log based process mining techniques can then be used to construct process models. A case study demonstrates the effectiveness of the presented approach. Results reveal that the preprocessing of the event logs considerably improves the derived process models.