Big Data Quality: A systematic literature review and future research directions <s> Comparative Evaluation <s> Organizations are nowadays immersed in the Big Data Era. Beyond the hype of the concept of Big Data, it is true that something in the way of doing business is really changing. Although some challenges keep being the same as for regular data, with big data, the focus has changed. The reason is due to Big Data is not only data, but also a complete framework including data themselves, storage, formats, and ways of provisioning, processing and analytics. A challenge that becomes even trickier is the one concerning to the management of the quality of big data. More than ever the need for assessing the quality-in-use of big datasets gains importance since the real contribution – business value- of a dataset to a business can be only estimated in its context of use. Although there exists different data quality models to assess the quality of data there still lacks of a quality-in-use model adapted to big data. To fill this gap, and based on ISO 25012 and ISO 25024, we propose the 3Cs model, which is composed of three data quality dimensions for assessing the quality-in-use of big datasets: Contextual Consistency, Operational Consistency and Temporal Consistency. <s> BIB001 </s> Big Data Quality: A systematic literature review and future research directions <s> Comparative Evaluation <s> This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review. <s> BIB002 </s> Big Data Quality: A systematic literature review and future research directions <s> Comparative Evaluation <s> ABSTRACTNowadays technological advances have promoted big data streams common in many applications, including mobile internet applications, internet of things, and industry production process. Outl... <s> BIB003
In this section, review papers that include Stream  - BIB003 and Batch BIB001 - BIB002 are explained in details and then compared. As mentioned earlier, there is no review papers in hybrid methods, hence, in 7.1 stream review papers, and in 7.2 batch review papers are discussed respectively. Finally, in 7.3 all review studies are compared.