Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
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DOI：
10.1016/j.ins.2014.01.015
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发表时间：
2014-08-10
影响因子：
8.1
通讯作者：
Zhang, Chun-Yang
中科院分区：
文献类型：
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作者：
Chen, C. L. Philip;Zhang, Chun-Yang
事实上，大数据已经引起了信息科学研究人员、政府和企业的政策和决策者的极大关注。在新世纪之初，随着信息增长的速度超过摩尔定律，过多的数据给人类带来了巨大的困扰。然而，在庞大的数据量中隐藏着如此多的潜在和高度有用的价值。一种新的科学范式诞生了，即数据密集型科学发现（DISD），也被称为大数据问题。从经济和商业活动到公共管理，从国家安全到许多领域的科学研究，许多领域和部门都涉及大数据问题。一方面，大数据对商业生产力的产生和科学学科的进化突破具有极大的价值，这给了我们很多机会在许多领域取得巨大进步。毫无疑问，未来商业生产力和技术的竞争必将汇聚到大数据的探索中。另一方面，大数据也面临着许多挑战，如数据捕获、数据存储、数据分析和数据可视化等方面的困难。本文旨在展示大数据的近景，包括大数据的应用，大数据的机遇和挑战，以及我们目前采用的最先进的技术和技术来处理大数据问题。我们还讨论了处理数据洪流的几种基本方法，例如，颗粒计算、云计算、生物启发计算和量子计算。(C) 2014爱思唯尔公司版权所有。
It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore's Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as data-intensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. (C) 2014 Elsevier Inc. All rights reserved.