Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Logic programming and databases
Logic programming and databases
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Dynamic change within workflow systems
COCS '95 Proceedings of conference on Organizational computing systems
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
A Machine Learning Approach to Workflow Management
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
A General Similarity Framework for Horn Clause Logic
Fundamenta Informaticae
Exploiting Inductive Logic Programming Techniques for Declarative Process Mining
Transactions on Petri Nets and Other Models of Concurrency II
Applying inductive logic programming to process mining
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Probabilistic declarative process mining
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Service Oriented Computing and Applications
Detecting implicit dependencies between tasks from event logs
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
A declarative approach for flexible business processes management
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
Multistrategy Operators for Relational Learning and Their Cooperation
Fundamenta Informaticae
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Standardized processes are important for correctly carrying out activities in an organization. Often the procedures they describe are already in operation, and the need is to understand and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution.