Applications of inductive logic programming
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: current challenges and future directions
Advances in knowledge discovery and data mining
Expert System Applications to Telecommunications
Expert System Applications to Telecommunications
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Evaluating Management Decisions via Delegation
Proceedings of the IFIP TC6/WG6.6 Third International Symposium on Integrated Network Management with participation of the IEEE Communications Society CNOM and with support from the Institute for Educational Services
A Hybrid Expert System/Neural Network Traffic Advice System
Proceedings of the IFIP TC6/WG6.6 Third International Symposium on Integrated Network Management with participation of the IEEE Communications Society CNOM and with support from the Institute for Educational Services
Fault Identification in Computer Network A Review and a New Approach
Fault Identification in Computer Network A Review and a New Approach
Automatically Acquiring Rules for Event Correlation from Event Logs
Automatically Acquiring Rules for Event Correlation from Event Logs
Towards Autonomic Computing: Effective Event Management
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Autonomic communications and the reflex unified fault management architecture
Advanced Engineering Informatics
Autonomic networks: engineering the self-healing property
Engineering Applications of Artificial Intelligence
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Telecommunication systems are built with extensive redundancy and complexity to ensure robustness and quality of service. Such systems requires complex fault identification and management tools. Fault identification and management are generally handled by reducing the number of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The goal is to determine and present the actual underlying fault. Fault management is a complex task, subject to uncertainty in the symptoms presented. In this paper two key fault management approaches are considered: (i) rule discovery to attempt to present fewer symptoms with greater diagnostic assistance for the more traditional rule based system approach and (ii) the induction of Bayesian Belief Networks (BBNs) for a complete 'intelligent' approach. The paper concludes that the research and development of the two target fault management systems can be complementary.