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Author Vinod, Gopika ♦ Tripathi, A. K. ♦ Singh, Lalit Kumar
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Reliability estimation ♦ System reliability ♦ Bayesian network
Abstract The computation of a set of performance indicators of a computer-based system can be achieved through dependability analysis. Researchers have proposed several methods and tools that have an ability to give a prognosis for the failure of a computer-based system. These tools and methods are classified into three main approaches: model-based, data-driven, and experience-based prognostics. Wherever sufficient real data is available, the data-driven approach is appropriate, which can be transformed into behavior models using Hidden Markov Models, which fall in a subclass of Bayesian networks. In a Bayesian framework, the estimates of reliabilities of components of a computer-based system are updated using operational profile data as new information of reliability of one or more node becomes available for the identification of robustness of a system. In this paper, we show, using Bayesian Networks, how to update the reliability of individual components and the reliability of a whole computer-based system when the reliability of any component in the system changes. We use a running safety-critical computer-based system from a nuclear power plant as a case study.
Description Affiliation: Dept of Computer Sc & Engg IIT (BHU) Varanasi, India (Singh, Lalit Kumar; Tripathi, A. K.) || Reactor Safety Division Bhabha Atomic Research Centre Dept of Atomic Energy, Govt of India (Vinod, Gopika)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1979-04-01
Publisher Place New York
Journal ACM SIGSOFT Software Engineering Notes (SOEN)
Volume Number 39
Issue Number 3
Page Count 6
Starting Page 1
Ending Page 6

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Source: ACM Digital Library