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Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016, and ... (Lecture Notes in Computer Science)

Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016, and ... (Lecture Notes in Computer Science)

Availability, Reliability, and Security in Information Systems: IFIP WG 8.4,
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Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016, and ... (Lecture Notes in Computer Science) Paperback - 2016

by Springer

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  • Title Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016, and ... (Lecture Notes in Computer Science)
  • Author Springer
  • Binding Paperback
  • Condition New
  • Pages 267
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2016-08-23
  • Illustrated Yes
  • Features Illustrated
  • Bookseller's Inventory # 27024424-n
  • ISBN 9783319455068 / 3319455060
  • Weight 0.87 lbs (0.39 kg)
  • Dimensions 9.21 x 6.14 x 0.59 in (23.39 x 15.60 x 1.50 cm)
  • Category Computers - General Information
  • Dewey Decimal Code 005.7
  • Quantity available 5

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From the publisher

This volume constitutes the refereed proceedings of the IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference on Availability, Reliability and Security in Information Systems, CD-ARES 2016, and the Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016, co-located with the International Conference on Availability, Reliability and Security, ARES 2016, held in Salzburg, Austria, in September 2016. The 13 revised full papers and 4 short papers presented were carefully reviewed and selected from 23 submissions. They are organized in the following topical sections: Web and semantics; diagnosis, prediction and machine learning; security and privacy; visualization and risk management; and privacy aware machine learning for health data science.


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