Big Data: Technologies, Challenges, Data Analytics and Management: A Review

S. Kaushik
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引用次数: 1

Abstract

Internet assumes an essential part in giving different learning sources to the world, which encourages numerous applications to give quality support of the customers. As the years go on the web is over-burden with parcel of data and it turns out to be difficult to extricate the applicable data from the web. This offers path to the advancement of the Big Data and the volume of the information continues expanding quickly step by step. Enormous Data has increased much consideration from the scholarly world and the IT business. In the advanced and figuring world, data is produced and gathered at a rate that quickly surpasses the limit go. Data mining procedures are utilized to locate the concealed data from the huge information. This Technique is utilized store, oversee, and investigate high speed of information and this information can be in any shape organized or unstructured frame. It is hard to handle substantial volume of information utilizing information base strategy like RDBMS. From one perspective, Big Data is amazingly important to deliver efficiency in organizations and transformative achievements in logical controls, which give us a considerable measure of chances to make incredible advances in many fields. There is most likely the future rivalries in business profitability and advances will without a doubt merge into the Big Data investigations. Then again, Big Data likewise emerges with many difficulties, for example, troubles in information catch, information stockpiling, information investigation and information perception. In this paper we concentrate on the audit of Big Data, its information order techniques and the way it can be mined utilizing different mining strategies.
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大数据:技术、挑战、数据分析与管理综述
互联网在向世界提供不同的学习资源方面起着至关重要的作用,这鼓励了许多应用程序为客户提供高质量的支持。随着时间的推移,网络承载着大量的数据,很难从网络中提取出适用的数据。这为大数据的发展提供了途径,信息的量也在逐步快速扩大。海量数据已经引起了学术界和IT行业的广泛关注。在这个先进和数字化的世界里,数据的产生和收集速度很快就超过了极限。利用数据挖掘程序从海量信息中找出隐藏的数据。该技术用于存储、监督和调查高速信息,这些信息可以是任何形状的有组织或无结构的框架。使用RDBMS这样的信息库策略很难处理大量的信息。从一个角度来看,大数据对于提高组织效率和逻辑控制方面的变革性成就非常重要,这为我们在许多领域取得令人难以置信的进步提供了相当大的机会。毫无疑问,未来在商业盈利能力和进步方面的竞争很可能会合并到大数据调查中。然而,大数据的出现也同样遇到了很多困难,比如信息捕捉、信息储存、信息调查、信息感知等方面的困难。在本文中,我们专注于大数据的审计,其信息顺序技术以及利用不同挖掘策略挖掘大数据的方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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