Software process validation: quantitatively measuring the correspondence of a process to a model
ACM Transactions on Software Engineering and Methodology (TOSEM)
Pip: detecting the unexpected in distributed systems
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
Conformance checking of processes based on monitoring real behavior
Information Systems
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Process compliance measurement based on behavioural profiles
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process diagnostics using trace alignment: Opportunities, issues, and challenges
Information Systems
Conformance Checking Using Cost-Based Fitness Analysis
EDOC '11 Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference
Monitoring business process compliance using compliance rule graphs
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Verification of GSM-Based artifact-centric systems through finite abstraction
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Data-aware process mining: discovering decisions in processes using alignments
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Modern organizations have invested in collections of descriptive and/or normative process models, but these rarely describe the actual processes adequately. Therefore, a variety of techniques for conformance checking have been proposed to pinpoint discrepancies between modeled and observed behavior. However, these techniques typically focus on the control-flow and abstract from data, resources and time. This paper describes an approach that aligns event log and model while taking all perspectives into account (i.e., also data, time and resources). This way it is possible to quantify conformance and analyze differences between model and reality. The approach was implemented using ProM and has been evaluated using both synthetic event logs and a real-life case study.