Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
The Knowledge Engineering Review
Discovering Operational Signatures with Time Constraints from a Discrete Event Sequence
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
MASON: A Proposal For An Ontology Of Manufacturing Domain
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
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AI Communications
International Journal of Knowledge-based and Intelligent Engineering Systems
Discovering Manufacturing Process from Timed Data: the BJT4R Algorithm
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A Global Method for Modelling and Performance Analysis of Production Flows
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
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In our increasingly competitive world, nowadays companies implement improvement strategies in every department and, in particular, in their manufacturing systems. This paper discusses the use of a global method, based on a knowledge-based approach, aiming at the development of a software tool for modeling and analysis of production flows. The main goal is the improvement of the performance of the production line. This method is based on data-processing and data-mining techniques and will help the acquisition of the meta-knowledge that is needed for finding correlations among different events in the line. Different techniques will be used: a graphical representation of the production, identification of specific behavior and research of correlations among events in the production line. Most of these techniques are based on statistical and probabilistic analyses. Events are expressed in the form of phenomena. To carry out high-level analyses, a stochastic approach will be used to identify breakdown models, which are the expression of specific correlations between phenomena. Breakdowns models will be the basis for, finally, defining action plans to improve the performance of the manufacturing lines.