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Uncertainty handling in big data using Fuzzy logic - Literature Review

EasyChair Preprint no. 4948, version 2

Versions: 12history
9 pagesDate: February 8, 2021


Advances in technology have gained wide attention from both academia and industry as Big Data plays a ubiquities and non-trivial role in the Data Analytical problems. Big Data analysis involves different types of uncertainty, and part of the uncertainty can be handled or at least reduced by fuzzy logic. In this work, we have reviewed a number of papers in detail, that have been published in the last decade, to identify the very recent and significant advancements including the breakthroughs in the field. We have noted that the vast majority of papers, most of the time, came up with methods that are less computational than the current methods that are available in the market and the proposed methods very often were better in terms of efficacy, cost-effectiveness and sensitivity. Needless to say that despite the existence of some works in the role of fuzzy logic in handling uncertainty, we have observed that few works have been done regarding how significantly uncertainty can impact the integrity and accuracy of big data.

Keyphrases: Big Data, Data Analytics, Fuzzy Logic, Uncertainty Handling

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Dyari M. Ameen M. Shareef and Sadegh Abollah Aminifar},
  title = {Uncertainty handling in big data using Fuzzy logic - Literature Review},
  howpublished = {EasyChair Preprint no. 4948},

  year = {EasyChair, 2021}}
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