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A Semiotic Approach to Investigate Quality Issues of Open Big Data Ecosystems

  • John Krogstie
  • Shang Gao
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 449)

Abstract

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.

Keywords

Big data data quality Semiotic levels 

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • John Krogstie
    • 1
  • Shang Gao
    • 1
  1. 1.Norwegian University of Science and Technology (NTNU)Norway

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