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Computational Intelligence Techniques for Assessing Data Quality: Towards Knowledge-Driven Processing

Afriliana, Nunik and Król, Dariusz and Gaol, Ford Lumban (2021) Computational Intelligence Techniques for Assessing Data Quality: Towards Knowledge-Driven Processing. CCS 2021: Computational Science – ICCS 2021. pp. 392-405.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract

Since the right decision is made from the correct data, assessing data quality is an important process in computational science when working in a data-driven environment. Appropriate data quality ensures the validity of decisions made by any decision-maker. A very promising area to overcome common data quality issues is computational intelligence. This paper examines from past to current intelligence techniques used for assessing data quality, reflecting the trend for the last two decades. Results of a bibliometric analysis are derived and summarized based on the embedded clustered themes in the data quality field. In addition, a network visualization map and strategic diagrams based on keyword co-occurrence are presented. These reports demonstrate that computational intelligence, such as machine and deep learning, fuzzy set theory, evolutionary computing is essential for uncovering and solving data quality issues.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 004 Computer Science (Cloud, Data Processing, Interface, E-mail, Social Media, Information Architecture, Hardware, Mobile Computing)
000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming (Algorithm, Programming Language, Applications, Software, Data Security)
Divisions: Fakultas Teknik Informatika > Program Studi Informatika
Depositing User: mr admin umn
Date Deposited: 12 Oct 2021 02:16
Last Modified: 12 Oct 2021 02:16
URI: http://kc.umn.ac.id/id/eprint/18667

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