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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wallin, S. Ahlund, C. Nordlander, J. |
| Copyright Year | 2012 |
| Description | Author affiliation: Luleå University of Technology 971 87 Luleå (Wallin, S.; Ahlund, C.; Nordlander, J.) |
| Abstract | Network Service Providers are struggling to reduce cost and still improve customer satisfaction. We have looked at three underlying challenges to achieve these goals; an overwhelming flow of low-quality alarms, understanding the structure and quality of the delivered services, and automation of service configuration. This thesis proposes solutions in these areas based on domain-specific languages, data-mining and self-learning. Most of the solutions have been validated based on data from a large service provider. We look at how domain-models can be used to capture explicit knowledge for alarms and services. In addition, we apply data-mining and self-learning techniques to capture tacit knowledge. The validation shows that models improve the quality of alarm and service models, and enables automatic rendering of functions like root cause correlation, service and SLA status, as well as service configuration. The data-mining and self-learning solutions show that we can learn from available decisions made by experts and automatically assign alarm priorities. |
| Starting Page | 880 |
| Ending Page | 886 |
| File Size | 337735 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467302678 |
| ISSN | 15421201 |
| e-ISBN | 9781467302692 |
| e-ISBN | 9781467302685 |
| DOI | 10.1109/NOMS.2012.6212003 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-04-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Object oriented modeling Monitoring Semantics Correlation Databases Switches Taxonomy neural networks network management data models data mining |
| Content Type | Text |
| Resource Type | Article |
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