Managing Communication Networks by Monitoring Databases
IEEE Transactions on Software Engineering
Effective management of local area networks: functions, instruments, and people
Effective management of local area networks: functions, instruments, and people
Multiprotocol networking—a blueprint
IBM Systems Journal
Local area networks—evolving from shared to switched access
IBM Systems Journal
Integrated management of networked systems: concepts, architectures, and their operational application
Network Protocol Handbook
Machine Learning
Learning from Multiple Bayesian Networks for the Revision and Refinement of Expert Systems
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Data abstractions for decision tree induction
Theoretical Computer Science
Learning from examples with unspecified attribute values
Information and Computation
Ecological interface design: a new approach for visualizing network management
Computer Networks: The International Journal of Computer and Telecommunications Networking
A distributed simulator for network resource management investigation
Computer Communications
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Several network troubles and/or malfunctions may occur due to the heavy traffic of recent computer networks. The discovering of some types of these troubles is not straightforward. Therefore, there is a real need to an intelligent system to recognize that type of problems using a priori background knowledge. The aim of this work is to present a network-monitoring utility that can discover various operational patterns and can provide sensible advice that may support the network administrator. It presents a machine learning system that can recognize network malfunctions. Such recognition process may be expressed in structured patterns to support network administrator for both problem solving and network management. To achieve this objective an explanation_based learning (EBL) procedure is used to obtain operational rules. In this case, the domain (network) knowledge is formally expressed and only one training example is analyzed in terms of this knowledge. This system uses a relational database to store and maintain the knowledge_base. The main contribution of the proposed system is to discover the abnormal patterns (malfunctions) of the network traffic. These abnormal patterns, as such, could be recognized from a real network using EBL. If the network administrator is advised with that malfunctions then he can adapt the current configuration in order to avoid the corresponding problems.