Authors:
Le Goc Marc
and
Benayadi Nabil
Affiliation:
LSIS, Laboratory for Sciences of Information and Systems, France
Keyword(s):
Temporal Knowledge Discovering, Markov Process, Information Thoeory, Knowledge Based Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
This paper is concerned with the discovery of expert’s knowledge from a sequence of alarms provided by a knowledge based system monitoring a dynamic process. The discovering process is based on the principles and the tools of the Stochastic Approach framework where a sequence is represented with a Markov chain from which binary relations between discrete event classes can be find and represented as abstract chronicle models. The problem with this approach is to reduce the search space as close as possible to the relations between the process variables. To this aim, we propose an adaptation of the J-Measure to the Stochastic Approach framework, the
BJ-Measure, to build an entropic based heuristic that help in finding abstract chronicle models revealing strong relations between the process variables. The result of the application of this approach to a real world system, the Sachem system that controls the blast furnace of the Arcelor-Mittal Steel group, is provided in the paper, show
ing how the combination of the Stochastic Approach and the Information Theory allows finding the a priori expert’s knowledge between blast furnace variables from a sequence of alarms.
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