Big Data Quality: A systematic literature review and future research directions
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One of the challenges manifested after global growth of social networks and the exponential growth of user-generated data is to identify user needs based on the data they share or tend to like. 'Big Data' is a term referring to data that exist in huge volume and various formats, i.e. structured or semi structured. The inherent features of this data have forced organizations to seek to identify desirable patterns amongst big data and make their fundamental decisions based on this information, in order to improve their customer services and enhance their business. As long as the big data that is being used is not of good quality, the business needs would not be expected to be met. As a result, big data quality needs to be taken into consideration seriously. Since there is no systematic review in the big data quality area, this study aims to present a systematic literature review of the research efforts on big data quality for those researchers who attempt to enter this area. In this systematic review, and after determining the basic requirements, a total of 419 studies are initially considered to be relevant. Then, with a review of the abstracts of the studies, 170 papers are included and ultimately after the complete study, 88 papers have been added to the final papers pool. Through careful study and analysis of these papers, the desired information has been extracted. As a result, a research tree is presented that divides the studies based on the type of processing, task, and technique. Then the active venues and other interesting profiles, as well as the classification of the new challenges of this field are discussed.
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