Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theoretical Computer Science
Journal of Automata, Languages and Combinatorics
Forming Grammars for Structured Documents: an Application of Grammatical Inference
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Experiments in Parallel Clustering with DBSCAN
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
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
A bibliographical study of grammatical inference
Pattern Recognition
ProM 4.0: comprehensive support for real process analysis
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Mining invisible tasks from event logs
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results 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
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The growing number of time-labeled datasets in science and industry increases the need for algorithms that automatically induce process models. Existing methods are capable of identifying process models that typically only work on single attribute events. We propose a new model type to address the problem of mining multi-attribute events, meaning that each event is described by a vector of attributes. The model is based on timed automata, includes expressive descriptions of states and can be used for making predictions. A probabilistic real time automaton is created, where each state is annotated by a profile of events. To identify the states of the automaton, similar events are combined by a clustering approach. The method was implemented and tested on a synthetic, a medical and a biological dataset. Its prediction accuracy was evaluated on a medical dataset and compared to a combined logistic regression, which is considered a standard in this application domain. Moreover, the method was experimentally compared to Multi-Output HMMs and Petri nets learned by standard process mining algorithms. The experimental comparison suggests that the automaton-based approach performs favorably in several dimensions. Most importantly, we show that meaningful medical and biological process knowledge can be extracted from such automata.