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Leveraging Analytics for Digital Transformation of Enterprise Services and Architectures
Reutlingen University, Germany.
Munich University, Germany.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. University of Rostock, Germany. (Information Engineering)
University of West Florida, USA.
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2016 (English)In: Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures, Springer, 2016, Vol. 111, 91-112 p.Chapter in book (Refereed)
Abstract [en]

The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud Computing, Big Data Mobile Systems, and Social Technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The Internet of Things, Social Collaboration Systems for Adaptive Case Management, Mobility Systems and Services for Big Data in Cloud Services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.

Place, publisher, year, edition, pages
Springer, 2016. Vol. 111, 91-112 p.
Series
Intelligent Systems Reference Library, ISSN 1868-4408 ; 111
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-34968DOI: 10.1007/978-3-319-40564-3_6Scopus ID: 2-s2.0-84988867457ISBN: 978-3-319-40562-9 (print)ISBN: 978-3-319-40564-3 (electronic)OAI: oai:DiVA.org:hj-34968DiVA: diva2:1070206
Available from: 2017-01-31 Created: 2017-01-31 Last updated: 2017-02-16Bibliographically approved

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Sandkuhl, Kurt

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CiteExportLink to record
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  • apa
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