Affiliations: [a] School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China | [b] Department d’Informatique, Université de N’Djamena, , N’Djamena, Tchad | [c] Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria
Correspondence:
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Corresponding author: Hongzhi Wang, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. E-mail: [email protected].
Abstract: Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).
Keywords: Big data, contextual data, data quality, similarity, Support Vector Machine, taxonomy
