• Corpus ID: 201870666

State of the Art on the Quality of Big Data: A Systematic Literature Review and Classification Framework

  title={State of the Art on the Quality of Big Data: A Systematic Literature Review and Classification Framework},
  author={Mostafa Mirzaie and Behshid Behkamal and Samad Paydar},
  journal={arXiv: Databases},
One of the most significant problems of Big Data is to extract knowledge through the huge amount of data. The usefulness of the extracted information depends strongly on data quality. In addition to the importance, data quality has recently been taken into consideration by the big data community and there is not any comprehensive review conducted in this area. Therefore, the purpose of this study is to review and present the state of the art on the quality of big data research through a… 
2 Citations

Big Data Quality: Factors, Frameworks, and Challenges

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The data characteristics of the big data environment are analyzed, quality challenges faced by big data are presented, and a hierarchical data quality framework is formulates from the perspective of data users.

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The overall findings indicate that there are no fundamentally new data quality issues in big data projects and the complexity of the issues is higher, which makes it harder to assess and attain data quality inbig data projects compared to the traditional projects.

Overview of data quality challenges in the context of Big Data

  • Suraj Juddoo
  • Computer Science
    2015 International Conference on Computing, Communication and Security (ICCCS)
  • 2015
Investigation of various components and activities forming part of data quality management such as dimensions, metrics, data quality rules, data profiling and data cleansing list existing challenges and future research areas associated with Big Data for dataquality management.

An Hybrid Approach to Quality Evaluation across Big Data Value Chain

A hybrid approach to Big Data quality evaluation across the Big Data value chain consists of assessing first the quality of Big Data itself, which involve processes such as cleansing, filtering and approximation, and then thequality of process handling this Big Data, which involves for example processing and analytics process.

Big Data Pre-processing: A Quality Framework

A QBD model incorporating processes to support Data quality profile selection and adaptation is proposed and it tracks and registers on a data provenance repository the effect of every data transformation happened in the pre-processing phase.

Data quality in big data processing: Issues, solutions and open problems

  • Pengcheng ZhangFang XiongJ. GaoJimin Wang
  • Computer Science
    2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
  • 2017
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