Name
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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Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11227-020-03300-3 | The Journal of Supercomputing |
Keywords | DocType | Volume |
SRNN, Data Quality Model Based-In-Use (DQMBIU), Metabolism, Big data quality | Journal | 77 |
Issue | ISSN | Citations |
2 | 0920-8542 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alladoumbaye Ngueilbaye | 1 | 2 | 1.10 |
| Hongzhi Wang | 2 | 421 | 73.72 |
| Mehak Khan | 3 | 0 | 1.69 |
| Daouda Ahmat Mahamat | 4 | 0 | 0.34 |