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The Challenges of Data Quality and Data Quality Assessment in the Big Data Era

机译:大数据时代数据质量和数据质量评估的挑战

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pHigh-quality data are the precondition for analyzing and using big data and for guaranteeing the value of the data. Currently, comprehensive analysis and research of quality standards and quality assessment methods for big data are lacking. First, this paper summarizes reviews of data quality research. Second, this paper analyzes the data characteristics of the big data environment, presents quality challenges faced by big data, and formulates a hierarchical data quality framework from the perspective of data users. This framework consists of big data quality dimensions, quality characteristics, and quality indexes. Finally, on the basis of this framework, this paper constructs a dynamic assessment process for data quality. This process has good expansibility and adaptability and can meet the needs of big data quality assessment. The research results enrich the theoretical scope of big data and lay a solid foundation for the future by establishing an assessment model and studying evaluation algorithms./p
机译:>高质量数据是分析和使用大数据并保证数据价值的前提。当前,缺乏对大数据质量标准和质量评估方法的综合分析和研究。首先,本文总结了数据质量研究的概况。其次,本文分析了大数据环境的数据特征,提出了大数据面临的质量挑战,并从数据用户的角度提出了分层的数据质量框架。该框架由大数据质量维度,质量特征和质量指标组成。最后,在此框架的基础上,本文构建了数据质量的动态评估过程。该过程具有良好的可扩展性和适应性,可以满足大数据质量评估的需求。通过建立评估模型和研究评估算法,研究成果丰富了大数据的理论范围,为未来奠定了坚实的基础。

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