Acta Scientific Computer Sciences

Short Communication Volume 3 Issue 8

Review of a Proposed Data Quality Measure

Andrew H Sung*

School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, USA

*Corresponding Author: Andrew H Sung, School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, USA.

Received: April 20, 2021; Published: July 13, 2021

Introduction

The rapidly growing big data collected or generated by various sources including public and private organizations, connected sensors, devices, the web and social network platforms, etc., have stimulated the advancement of data science, which holds tremendous potential for problem solving in diverse domains. Various methods and techniques have been developed to carry out data analytics and data mining for different purposes, where an ultimate goal of data mining is knowledge discovery pertaining to problems that have thus far defied solutions through conventional analytical approaches.

Bibliography

  1. B Ribeiro., et al. “Critical Feature Selection and Critical Sampling for Data Mining”. Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation (Ganapathi, G. et al., Editors) Springer CCIS Series (2018): 13-24.

Citation

Citation: Andrew H Sung. “Review of a Proposed Data Quality Measure". Acta Scientific Computer Sciences 3.8 (2021): 32-33.

Copyright

Copyright: © 2021 Andrew H Sung. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Acceptance rate35%
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