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Volumn , Issue , 2007, Pages 475-481

Making life better one large system at a time: Challenges for UAI research

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTED SYSTEMS; INVITED TALK; LARGE SYSTEM; MANAGEMENT CHALLENGES; MEASUREMENT TECHNOLOGIES; OPEN PROBLEMS; POLICY OPTIMIZATION; RAPID GROWTH; SERVICE OFFERING;

EID: 80053214143     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1)

References (24)
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    • Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57, 1995.
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    • Benjamini, Y.1    Hochberg, Y.2
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    • 80053204818 scopus 로고    scopus 로고
    • Testing of a point null hypothesis: The irreconciability of significance levels and evidence (with discussion)
    • J. Berger and T. Sellke. Testing of a point null hypothesis: The irreconciability of significance levels and evidence (with discussion). Journal of American Statistics Association, 82, 1987.
    • Journal of American Statistics Association , vol.82 , pp. 1987
    • Berger, J.1    Sellke, T.2
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    • Recovery-oriented computing: Building multitier dependability
    • G. Candea, A. Brown, A. Fox, and D. Patterson. Recovery-oriented computing: building multitier dependability. Computer, 37, 2004.
    • Computer , vol.37 , pp. 2004
    • Candea, G.1    Brown, A.2    Fox, A.3    Patterson, D.4
  • 10
    • 34249832377 scopus 로고    scopus 로고
    • A Bayesian method for the induction of probabilistic models from data
    • G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic models from data. Machine Learning, 9, 1992.
    • Machine Learning , vol.9 , pp. 1992
    • Cooper, G.1    Herskovits, E.2
  • 14
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about network structure
    • N. Friedman and D. Koller. Being Bayesian about network structure. Machine Learning, 50(1-2), 2003.
    • (2003) Machine Learning , vol.50 , Issue.1-2
    • Friedman, N.1    Koller, D.2
  • 15
    • 34249761849 scopus 로고    scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • D. Heckerman, D. Geiger, and M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 1995.
    • Machine Learning , vol.20 , pp. 1995
    • Heckerman, D.1    Geiger, D.2    Chickering, M.3
  • 17
    • 71749096263 scopus 로고    scopus 로고
    • Autopilot: Automatic data center management
    • M. Isard. Autopilot: Automatic data center management. Operating Systems Review, 41(2), 2007.
    • (2007) Operating Systems Review , vol.41 , Issue.2
    • Isard, M.1
  • 18
    • 0037253062 scopus 로고    scopus 로고
    • The vision of autonomic computing
    • J. Kephard and D. Chess. The vision of autonomic computing. Computer, 36, 2003.
    • Computer , vol.36 , pp. 2003
    • Kephard, J.1    Chess, D.2
  • 21
    • 84950945692 scopus 로고    scopus 로고
    • Model selection and accounting for model uncertainty in graphical models using occam's window
    • D. Madigan and A. Raftery. Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal of the American Statistical Association, 89, 1994.
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    • Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining
    • R. van Renesse, K. Birman, and W. Vogels. Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining. ACM Transactions on Computer Systems, 21, 2003.
    • ACM Transactions on Computer Systems , vol.21 , pp. 2003
    • Van Renesse, R.1    Birman, K.2    Vogels, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.