Endeavours for adopting network data greatly from social media allude to give the specific methods in extracting value from data space, for example, conversion, transaction, message and others, where structured information sources originate from big business assets information and unstructured information sources originate from video and audio. It very well may be accomplished to extend the way toward extracting value from social network for designing the information sources to satisfy the association objective. This paper means to uncover the method for approach of big data in extracting information esteem from information complexity including velocity and variety into volume. This investigation was led employing contents analysis by looking into certain literary works in chapters, books and peer-audited journals and procedures in creating prototype employing data analytics related from users, time analytics and topic. The discoveries uncover that big data rising technology with analytics procedure gives specific favourable circumstances to change the trend of data fitted into innovative atmosphere of OLR i.e. online learning resources to upgrade in building up learning resources. Both model and prototype information extraction value can be upgraded to encourage the learning atmosphere in supporting the implementations with convenience and ease. This investigation is relied upon to add to improve the outcomes and learning atmosphere with achievement and performance by upgrading the learning process development of students to give online resources in the higher education setting.

With the recent advancement in the field of machine learning, health synthetic data has become a promising technique to address difficulties with time consumption when accessing and using electronic medical records for research and innovations. However, health synthetic data utility and governance have not been extensively studied. A scoping review was conducted to understand the status of evaluations and governance of health synthetic data following the PRISMA guidelines. The results showed that if synthetic health data are generated via proper methods, the risk of privacy leaks has been low and data quality is comparative to real data. However, the generation of health synthetic data has been generated on a case-by-case basis instead of being scaled up. Furthermore, regulations, ethics, and data sharing of health synthetic data have primarily been inexplicit, although common principles for sharing such data do exist.