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19 mars 2015
- handle: 10670/1.juhhth
- hal: hal-01324960
- doi: 10.1007/978-3-319-16274-4_5
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-16274-4_5
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Big data data quality Semiotic levelsSujets proches
Large data sets Data sets, LargeCiter ce document
John Krogstie et al., « A Semiotic Approach to Investigate Quality Issues of Open Big Data Ecosystems », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-319-16274-4_5
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Résumé
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.