Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016, Proceedings

Francesco Buccafurri (Herausgeber), Andreas Holzinger (Herausgeber), Peter Kieseberg (Herausgeber), A Min Tjoa (Herausgeber), Edgar Weippl (Herausgeber)

Publikation: Buch/Bericht/KonferenzbandBuch (Autorenwerk)ForschungBegutachtung

Abstract

The Cross-Domain Conference and Workshop CD-ARES is focused on the holistic and scientific view for applications in the domain of information systems.
The idea of organizing cross-domain scientific events originated from a concept presented by the IFIP president Leon Strous at the IFIP 2010 World Computer Congress in Brisbane, which was seconded by many IFIP delegates in further discussions. Therefore CD-ARES concentrates on the multitudinous aspects of information systems, in bridging the gap between the research results in computer science and the many applicationfields.
This effort leads us to the consideration of the various important issues of massive information sharing and data integration, which will (in our opinion) dominate scientific work and discussions in the area of information systems in the second decade of this century. The organizers of this event, who are engaged within IFIP in the area of Enterprise Information Systems (WG 8.9), Business Information Systems (WG 8.4), and Information Technology Applications (TC 5), very much welcome the typical cross-domain aspect of this event. To guarantee a high-quality event, we assembled a program for CD-ARES 2016 consisting of 12 selected papers. CD-ARES 2016 provided a good mix of topics ranging from knowledge management and software security to mobile and social computing.
Machine learning is the fastest growingfield in computer science, and health informatics is among the greatest challenges, where privacy, data protection, safety,
information security, and fair use of data is of utmost importance. Experts of work area 1 (data science), 2 (machine learning), and 7 (privacy) of the international expert network HCI-KDD carefully selected five papers for the PAML (Privacy Aware Machine Learning) session.
Originalspracheenglisch
ErscheinungsortHeidelberg, Berlin
VerlagSpringer
AuftraggeberInternational Federation for Information Processing
Seitenumfang300
Band9817
ISBN (elektronisch)978-3-319-45507-5
ISBN (Print)978-3-319-45506-8
DOIs
PublikationsstatusVeröffentlicht - 2016

Publikationsreihe

NameLecture Notes in Computer Science
Herausgeber (Verlag)Springer
Band9817
ISSN (Druck)0302-9743
ISSN (elektronisch)1611-3349

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Learning systems
Information systems
Health
Availability
Computer science
Data privacy
Data integration
Security of data
Human computer interaction
Knowledge management
Information technology
Industry

Schlagwörter

    ASJC Scopus subject areas

    • Information systems

    Fields of Expertise

    • Information, Communication & Computing

    Treatment code (Nähere Zuordnung)

    • Basic - Fundamental (Grundlagenforschung)
    • Experimental

    Dies zitieren

    Availability, Reliability, and Security in Information Systems : IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016, Proceedings. / Buccafurri, Francesco (Herausgeber); Holzinger, Andreas (Herausgeber); Kieseberg, Peter (Herausgeber); Tjoa, A Min (Herausgeber); Weippl, Edgar (Herausgeber).

    Heidelberg, Berlin : Springer, 2016. 300 S. (Lecture Notes in Computer Science; Band 9817).

    Publikation: Buch/Bericht/KonferenzbandBuch (Autorenwerk)ForschungBegutachtung

    Buccafurri, Francesco (Herausgeber) ; Holzinger, Andreas (Herausgeber) ; Kieseberg, Peter (Herausgeber) ; Tjoa, A Min (Herausgeber) ; Weippl, Edgar (Herausgeber). / Availability, Reliability, and Security in Information Systems : IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016, Proceedings. Heidelberg, Berlin : Springer, 2016. 300 S. (Lecture Notes in Computer Science).
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