Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Proceedings of the Conference on Logic of Programs
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Exploiting Inductive Logic Programming Techniques for Declarative Process Mining
Transactions on Petri Nets and Other Models of Concurrency II
Robust Process Discovery with Artificial Negative Events
The Journal of Machine Learning Research
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Efficient discovery of understandable declarative process models from event logs
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
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Process mining techniques can be used to discover process models from event data. Often the resulting models are complex due to the variability of the underlying process. Therefore, we aim at discovering declarative process models that can deal with such variability. However, for real-life event logs involving dozens of activities and hundreds or thousands of cases, there are often many potential constraints resulting in cluttered diagrams. Therefore, we propose various techniques to prune these models and remove constraints that are not interesting or implied by other constraints. Moreover, we show that domain knowledge (e.g., a reference model or grouping of activities) can be used to guide the discovery approach. The approach has been implemented in the process mining tool ProM and evaluated using an event log from a large Dutch hospital. Even in such highly variable environments, our approach can discover understandable declarative models.