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RETRACTED ARTICLE: Adoption of human metabolic processes as Data Quality Based Models

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This article was retracted on 15 July 2024

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Abstract

The buildup of huge data within business intelligence is essential because such data includes complete conceptual and technological stack in addition to raw and processed data, data management, and analytics. Evaluating Data Quality Model Based-In-Use has gained more ground since business value could be only estimated in its used context. Despite the numerous data quality models used for regular data quality assessment, none of them have been amended to big data. For this reason, we propose four efficiencies and four metabolism processes as data quality indicators usable in big data researches. This model appropriately obtained the quality in use levels of the entry data for big data analytics, and those adequacies of Data Quality Model Based-In-Use levels could be comprehended as dependability indicators and adequacy of big data investigation. Besides, we have demonstrated the practical examples along with a proposed method, the stacked recurrent neural network for data quality assessment. Therefore, this model being independent of any pre-conditions or technologies could be integrated into various big data research.

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Acknowledgements

This paper was partially funded by the National Key R&D Program of China under Grant Nos. 2018YFB1004700, and NSFC Grant Nos. U1866602, 61602129, 61772157.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, P. Box 75, Harbin, China

    Alladoumbaye Ngueilbaye, Hongzhi Wang & Mehak Khan

  2. Departement d’Informatique, Universite de N’Djamena (Tchad), Avenue Mobutu, B.P. 1117, N’Djamena, Chad

    Daouda Ahmat Mahamat

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  1. Alladoumbaye Ngueilbaye
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Correspondence to Hongzhi Wang.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-024-06358-5"

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Ngueilbaye, A., Wang, H., Khan, M. et al. RETRACTED ARTICLE: Adoption of human metabolic processes as Data Quality Based Models. J Supercomput 77, 1779–1817 (2021). https://doi.org/10.1007/s11227-020-03300-3

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