Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Guest Editorial Special Section on Engineering Industrial Big Data Analytics Platforms for Internet of Things
School of Computing Science, Newcastle University, Newcastle, UK.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
Chalmers University, Gothenburg, Sweden.
Department of Computing, Macquarie University, Sydney, Australia.
Show others and affiliations
2018 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 14, no 2, p. 744-747Article in journal, Editorial material (Refereed) Published
Abstract [en]

Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 14, no 2, p. 744-747
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-69565DOI: 10.1109/TII.2017.2788080ISI: 000424483600034Scopus ID: 2-s2.0-85042397905OAI: oai:DiVA.org:ltu-69565DiVA, id: diva2:1218984
Note

Alternative title: Special Section on Engineering Industrial Big Data Analytics Platforms for Internet of Things

Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2019-04-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Vasilakos, Athanasios

Search in DiVA

By author/editor
Vasilakos, Athanasios
By organisation
Computer Science
In the same journal
IEEE Transactions on Industrial Informatics
Media and Communication Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 11 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf