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A Semiotic Approach to Investigate Quality Issues of Open Big Data Ecosystems
Norwegian University of Science and Technology, Trondheim, Norway.
Norwegian University of Science and Technology, Trondheim, Norway.ORCID iD: 0000-0002-3722-6797
2015 (English)In: Information and Knowledge Management in Complex Systems: 16th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2015, Toulouse, France, March 19-20, 2015. Proceedings / [ed] Liu, Kecheng; Nakata, Keiichi; Li, Weizi; Galarreta, Daniel, Springer Berlin/Heidelberg, 2015, 41-50 p.Chapter in book (Refereed)
Abstract [en]

The quality of data models has been investigated since the mid-nineties. In another strand of research, data and information quality has been investigated even longer. Data can also be looked upon as a type of model (on the instance level), as illustrated e.g. in the product models in CAD-systems. We have earlier presented a specialization of the general SEQUAL-framework to be able to evaluate the combined quality of data models and data. In this paper we look in particular on the identified issues of ‘Big Data’. We find on the one hand that the characteristics of quality of big data can be looked upon in the light of the quality levels of the SEQUAL-framework as it is specialized for data quality, and that there are aspects in this framework that are not covered by the existing work on big data. On the other hand, the exercise has resulted in a useful deepening of the generic framework for data quality, and has in this way improved the practical applicability of the SEQUAL-framework when applied to discussing and assessing quality of big data.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. 41-50 p.
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 449
Keyword [en]
Big data, data quality, Semiotic levels
National Category
Information Systems
Identifiers
URN: urn:nbn:se:oru:diva-53163DOI: 10.1007/978-3-319-16274-4_5ISBN: 978-3-319-16274-4OAI: oai:DiVA.org:oru-53163DiVA: diva2:1039355
Available from: 2016-10-24 Created: 2016-10-24 Last updated: 2016-10-25Bibliographically approved

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Gao, Shang
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