Big Data analysis and processing is a popular tool for Artificial Intelligence and Data Science to extract applicable solution from data across a broad range of application domains. Even though Big data is in the mainstream of operations as of 2020, With the increase in data processing and storage capacity, a large amount of data is available and because of that potential issues or challenges the researchers can address, some of these issues overlap with the data science field. One of the key issue is the inevitable existence of uncertainty in stored or missing values. Any uncertainty in a source causes its disadvantageous, complexity or inapplicability to use. It is importance to ensure the reliability and a value of data source. That is why it is crucial to eliminate uncertainty or to lower uncertainty influence because data without any analysis does not have much value. In this paper we review previous work in big data analytics and Survey of many theories and techniques which have been developed to model its various forms. We have described several common techniques such as Bayesian model and fuzzy set, Shannon’s entropy. We present a discussion of open challenges and future directions for handling and eliminating uncertainty in this profile.

Big Data, Data Sciences, Data Uncertainty, Uncertainty Elimination, Machine learning, NLP, Computational Intelligence.

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