Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
DECLARE: Full Support for Loosely-Structured Processes
EDOC '07 Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference
Automatic generation of software behavioral models
Proceedings of the 30th international conference on Software engineering
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
Applying inductive logic programming to process mining
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
BPM'06 Proceedings of the 4th international conference on Business Process Management
A declarative approach for flexible business processes management
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
Efficient discovery of understandable declarative process models from event logs
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
Discovering branching conditions from business process execution logs
FASE'13 Proceedings of the 16th international conference on Fundamental Approaches to Software Engineering
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A wealth of techniques are available to automatically discover business process models from event logs. However, the bulk of these techniques yield procedural process models that may be useful for detailed analysis, but do not necessarily provide a comprehensible picture of the process. Additionally, barring few exceptions, these techniques do not take into account data attributes associated to events in the log, which can otherwise provide valuable insights into the rules that govern the process. This paper contributes to filling these gaps by proposing a technique to automatically discover declarative process models that incorporate both control-flow dependencies and data conditions. The discovered models are conjunctions of first-order temporal logic expressions with an associated graphical representation (Declare notation). Importantly, the proposed technique discovers underspecified models capturing recurrent rules relating pairs of activities, as opposed to full specifications of process behavior --- thus providing a summarized view of key rules governing the process. The proposed technique is validated on a real-life log of a cancer treatment process.