A Study of Handling Missing Data Methods for Big Data

Improving data quality is not a recent field but in the context of big data this is a challenging area as there is a crucial need for data quality to, for example, increase the accuracy of big data analytics or avoid storing redundant data. Missing data is one of the major problem that faces the quality of data. There are several methods and approaches that have been used in relational databases to handle missing data most of which have been adapted to big data. This paper aims to provide an overview of some methods and approaches for handling missing data in big data contexts.