Methods for detecting and correcting contextual data quality problems
| Autor: | Daouda Ahmat Mahamat, Sahalu B. Junaidu, Ibrahim A. Elgendy, Hongzhi Wang, Alladoumbaye Ngueilbaye |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre Theoretical Computer Science Contextual design Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Computer Vision and Pattern Recognition Artificial intelligence business computer media_common |
| Zdroj: | Intelligent Data Analysis. 25:763-787 |
| ISSN: | 1571-4128 1088-467X |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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