Zaidi, H., Boufarès, F., Pollet, Y. and Kraiem, N. (2015) Semantic of Data Dependencies to Improve the Data Quality. Proceedings of the 5th International Conference on Model & Data Engineering, LNCS, Vol. 9344, Springer, Rhodes Greece, 26-28 September 2015, 53-61.

has been cited by the following article:

• TITLE: Improve Data Quality by Processing Null Values and Semantic Dependencies

  AUTHORS: Houda Zaidi, Faouzi Boufarès, Yann Pollet

  KEYWORDS: Data Quality, Big Data, Contextual Semantics, Semantic Dependencies, Functional Dependencies, Null Values, Data Cleaning

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.5, May 26, 2016

  ABSTRACT: Today, the quantity of data continues to increase, furthermore, the data are heterogeneous, from multiple sources (structured, semi-structured and unstructured) and with different levels of quality. Therefore, it is very likely to manipulate data without knowledge about their structures and their semantics. In fact, the meta-data may be insufficient or totally absent. Data Anomalies may be due to the poverty of their semantic descriptions, or even the absence of their description. In this paper, we propose an approach to better understand the semantics and the structure of the data. Our approach helps to correct automatically the intra-column anomalies and the inter-col- umns ones. We aim to improve the quality of data by processing the null values and the semantic dependencies between columns.