C4.5: programs for machine learning
C4.5: programs for machine learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Process Models from Unlabelled Event Logs
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Discovering Process Models from Unlabelled Event Logs
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Correlation patterns in service-oriented architectures
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Event correlation for process discovery from web service interaction logs
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering event correlation rules for semi-structured business processes
Proceedings of the 5th ACM international conference on Distributed event-based system
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
Data transformation and semantic log purging for process mining
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Efficient discovery of understandable declarative process models from event logs
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Techniques for a Posteriori Analysis of Declarative Processes
EDOC '12 Proceedings of the 2012 IEEE 16th International Enterprise Distributed Object Computing Conference
Plug-and-Play Virtual Factories
IEEE Internet Computing
Aligning event logs and declarative process models for conformance checking
BPM'12 Proceedings of the 10th international conference on Business Process Management
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Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.