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Article summary:
1. This article presents a comprehensive overview of Big Data Analytics (BDA) and its components, including data generation, acquisition, storage, analytics, visualization and decision-making for value-creation.
2. It explores the seven V's characteristics of BDA - Volume, Velocity, Variety, Valence, Veracity, Variability and Value - as well as various big data analytics tools and techniques.
3. The paper also highlights the applications of BDA in various domains such as agriculture, healthcare, cyber security and smart cities.
Article analysis:
The article provides a comprehensive overview of Big Data Analytics (BDA) and its components. It is well-informed with realistic analysis for deploying BDA successfully in companies. The seven V's characteristics of BDA are explored in detail along with various big data analytics tools and techniques. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security and smart city.
The article is reliable in terms of providing an overview of the architecture of BDA including six components; exploring the seven V's characteristics; describing big data analytics tools; presenting a methodical analysis for the usage of Big Data Analytics; highlighting previous research; discussing challenges; noting current status; and suggesting future directions. However there are some potential biases that need to be considered when evaluating this article’s trustworthiness and reliability. For example, there is no mention or discussion about possible risks associated with using Big Data Analytics or any counterarguments to the claims made in the article. Additionally there is no evidence provided to support some of the claims made which could weaken its credibility. Moreover there is no indication that both sides have been presented equally which could lead to partiality or one-sided reporting in some areas. Finally there may be promotional content present which could influence readers’ opinions without providing sufficient evidence or facts to back up these claims.