دانلود رایگان مقاله انگلیسی نظرسنجی به منظور ادغام تحلیل داده های بزرگ جهت بینش بزرگ برای ایجاد ارزش - الزویر 2018

ABSTRACT

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decisionmaking. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. 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. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

 Conclusions and research directions

In this article, a theoretically sound framework for value creation by using Big Data Analytics is presented. The objective of this article is to bridge the gap by focusing on value-discover, value-creation, and value-realization by using big data management, processing and advanced analytics. Because, there are numerous challenges for traditional analytics in terms of scalability, adaptability, and usability, presenting new opportunities for inspiring enterprises to adopt BDA for it for decision-making. The answers to RQs used in our paper are as follows: By considering the first research question, BDA explored the most important seven characteristics namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value. A large research is being done in defining BDA in terms of Vs for data challenges. BDA has been prospected to raise the economic returns by gaining deeper insights from mountains of existing data. Our response to second research question has provided an overview of the architecture of BDA-DM framework including six components, namely (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value creation. Our third research question has presented the detailed information of BDA tools, techniques and technologies. This field has received much attention due to its wide application as multi-purpose tools, borrowing techniques from Natural Language Processing (NLP), Data Mining (DM), Machine Learning (ML), Deep Learning (DL) etc. Currently, benchmarking software technologies such as e.g. (Hadoop/Map-Reduce based processing frameworks), NoSQL databases, graph data-bases and analytical frameworks have been developed for BDA.