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Combining high level symptom descriptions and low level state information for configuration fault diagnosis

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Automatic fault diagnosis is an important problem for system management. In this paper, we combine high level symptom descriptions and low level state information to solve the system fault diagnosis problem. We extract state-symptom correlation information from knowledge sources in text format, and then use symptom similarity to rank the candidate system states. We apply the method to Windows Registry problems to help Product Support Service (PSS) engineers. Promising results with two different knowledge sources show the robustness of our method. Finally, we explain why this combination is successful and also discuss its limitations. © LISA 2004.All right reserved.
Original languageEnglish
Title of host publicationLISA 2004 - 18th Large Installation System Administration Conference
PublisherUSENIX Association
Pages151-157
Publication statusPublished - 2004
Externally publishedYes
Event18th Large Installation System Administration Conference, LISA 2004 - Atlanta, United States
Duration: 14 Nov 200419 Nov 2004

Publication series

NameLISA 2004 - 18th Large Installation System Administration Conference

Conference

Conference18th Large Installation System Administration Conference, LISA 2004
PlaceUnited States
CityAtlanta
Period14/11/0419/11/04

Bibliographical note

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