An overview of workflow management: from process modeling to workflow automation infrastructure
Distributed and Parallel Databases - Special issue on software support for work flow management
Machine Learning - special issue on inductive logic programming
Robust Classification for Imprecise Environments
Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Machine Learning
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Verifiable agent interaction in abductive logic programming: The SCIFF framework
ACM Transactions on Computational Logic (TOCL)
Exploiting Inductive Logic Programming Techniques for Declarative Process Mining
Transactions on Petri Nets and Other Models of Concurrency II
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Inducing declarative logic-based models from labeled traces
BPM'07 Proceedings of the 5th international conference on Business process management
Applying inductive logic programming to process mining
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Probabilistic inductive logic programming
A declarative approach for flexible business processes management
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
DecSerFlow: towards a truly declarative service flow language
WS-FM'06 Proceedings of the Third international conference on Web Services and Formal Methods
A Logic Framework for Incremental Learning of Process Models
Fundamenta Informaticae
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The management of business processes is receiving much attention, since it can support significant efficiency improvements in organizations. One of the most interesting problems is the representation of process models in a language that allows to perform reasoning on it. Various knowledge-based languages have been lately developed for such a task and showed to have a high potential due to the advantages of these languages with respect to traditional graph-based notations. In this work we present an approach for the automatic discovery of knolwedge-based process models expressed by means of a probabilistic logic, starting from a set of process execution traces. The approach first uses the DPML (Declarative Process Model Learner) algorithm [16] to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights of each formula are tuned using the Alchemy system. The resulting theory allows to perform probabilistic classification of traces. We tested the proposed approach on a real database of university students' careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.