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Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment

Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment

Pradheep Kumar K., Venkata Subramanian D.
Copyright: © 2017 |Pages: 23
ISBN13: 9781522510086|ISBN10: 1522510087|EISBN13: 9781522510093
DOI: 10.4018/978-1-5225-1008-6.ch001
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MLA

Pradheep Kumar K., and Venkata Subramanian D. "Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment." Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, edited by Arun Kumar Sangaiah, et al., IGI Global, 2017, pp. 1-23. https://doi.org/10.4018/978-1-5225-1008-6.ch001

APA

Pradheep Kumar K. & Venkata Subramanian D. (2017). Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment. In A. Sangaiah, X. Gao, & A. Abraham (Eds.), Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making (pp. 1-23). IGI Global. https://doi.org/10.4018/978-1-5225-1008-6.ch001

Chicago

Pradheep Kumar K., and Venkata Subramanian D. "Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment." In Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, edited by Arun Kumar Sangaiah, Xiao-Zhi Gao, and Ajith Abraham, 1-23. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1008-6.ch001

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Abstract

This paper is intended to design a fuzzy based approach to assess standards and quality of big data. It also serves as a platform to organizations that intend to migrate their existing database environment to big data environment. Data is assessed using a multidimensional approach based on quality factors like accuracy, completeness, reliability, usability, etc. These factors are analysed by constructing decision trees to identify the quality aspects which need to be improved. In this work fuzzy queries have been designed. The queries are grouped as sets namely Excellent, Optimal, Fair and Hybrid. Based on the fuzzy data sets formed and the query compatibility index, a query set is chosen. A data set that has a very high degree of membership is assigned a fair query set. A data set with a medium degree of membership is assigned a optimal query set. A data set that has a lesser degree of membership is assigned a Excellent query set. A data set which needs a combination of queries of all the above is assigned a hybrid query set. The fuzzy query based approach reduces the query compatibility index by 36%, compared to a normal query set approach.

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