Discovering patterns in sequences of events
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to genetic algorithms
An introduction to genetic algorithms
ANSWER: network monitoring using object-oriented rules
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining and Forecasting in Large-Scale Telecommunication Networks
IEEE Expert: Intelligent Systems and Their Applications
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Predicting web actions from HTML content
Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Understanding Web Usage for Dynamic Web-Site Adaptation: A Case Study
WSE '02 Proceedings of the Fourth International Workshop on Web Site Evolution (WSE'02)
Analysis of web-usage behavior for focused web sites: a case study
Journal of Software Maintenance and Evolution: Research and Practice - Special issue: Web site evolution
Topographical proximity for mining network alarm data
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A survey of online failure prediction methods
ACM Computing Surveys (CSUR)
Intelligent agents for real time data mining in telecommunications networks
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Predicting failures of computer systems: a case study for a telecommunication system
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
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The computer and telecommunication industries rely heavily on knowledge-based expert systems to manage the performance of their networks. These expert systems are developed by knowledge engineers, who must first interview domain experts to extract the pertinent knowledge. This knowledge acquisition process is laborious and costly, and typically is better at capturing qualitative knowledge than quantitative knowledge. This is a liability, especially for domains like the telecommunication domain, where enormous amounts of data are readily available for analysis. Data mining holds tremendous promise for the development of expert systems for monitoring network performance since it provides a way of automatically identifying subtle, yet important, patterns in data. This case study describes a project in which a temporal data mining system called Timeweaver is used to identify faulty telecommunication equipment from logs of network alarm messages.