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Semantic-Driven Model Composition for Accurate Anomaly Diagnosis Saeed Ghanbari and Cristiana Amza
 

Summary: Semantic-Driven Model Composition for Accurate Anomaly Diagnosis
Saeed Ghanbari and Cristiana Amza
Department of Electrical and Computer Engineering
University of Toronto
{saeed,amza}@eecg.toronto.edu
Abstract
In this paper, we introduce a semantic-driven ap-
proach to system modeling for improving the accuracy of
anomaly diagnosis. Our framework composes heteroge-
neous families of models, including generic statistical mod-
els, and resource-specific models into a belief network, i.e.,
Bayesian network. Given a set of models which sense the
behavior of various system components, the key idea is to in-
corporate expert knowledge about the system structure and
dependencies within this structure, as meta-correlations
across components and models. Our approach is flexible,
easily extensible and does not put undue burden on the sys-
tem administrator. Expert beliefs about the system hierar-
chy, relationships and known problems can guide learning,
but do not need to be fully specified. The system dynamically

  

Source: Amza, Cristiana - Department of Electrical and Computer Engineering, University of Toronto

 

Collections: Computer Technologies and Information Sciences