Quality Assurance in Big Data Engineering - A Metareview

Daniel Staegemann, Matthias Volk, Klaus Turowski


With a continuously increasing amount and complexity of data being produced and captured, traditional ways of dealing with their storing, processing, analysis and presentation are no longer sufficient, which has led to the emergence of the concept of big data. However, not only the implementation of the corresponding applications is a challenging task, but also the proper quality assurance. To facilitate the latter, in this publication, a comprehensive structured literature metareview on the topic of big data quality assurance is presented. The results will provide interested researchers and practitioners with a solid foundation for their own quality assurance related endeavors and therefore help in advancing the cause of quality assurance in big data as well as the domain of big data in general. Furthermore, based on the findings of the review, worthwhile directions for future research were identified, providing prospective authors with some guidance in this complex environment.


Big data; Quality Assurance; Benchmark; Testing; Literature Review; Metareview

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DOI: 10.7250/csimq.2021-28.01


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Copyright (c) 2021 Daniel Staegemann, Matthias Volk, Klaus Turowski

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