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Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
arXiv - CS - Other Computer Science Pub Date : 2021-04-12 , DOI: arxiv-2104.05765
Hrishav Bakul Barua, Kartick Chandra Mondal

Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable impact from it. The cloud computing paradigm has been targeted for big data processing and mining in a more efficient manner using the plethora of resources available from computing nodes to efficient storage. Cloud data mining introduces the concept of performing data mining and analytics of huge data in the cloud availing the cloud resources. But can we do better? Yes, of course! The main contribution of this chapter is the identification of four game-changing technologies for the acceleration of computing and analysis of data mining tasks in the cloud. Graphics Processing Units can be used to further accelerate the mining or analytic process, which is called GPU accelerated analytics. Further, Approximate Computing can also be introduced in big data analytics for bringing efficacy in the process by reducing time and energy and hence facilitating greenness in the entire computing process. Quantum Computing is a paradigm that is gaining pace in recent times which can also facilitate efficient and fast big data analytics in very little time. We have surveyed these three technologies and established their importance in big data mining with a holistic architecture by combining these three game-changers with the perspective of big data. We have also talked about another future technology, i.e., Neural Processing Units or Neural accelerators for researchers to explore the possibilities. A brief explanation of big data and cloud data mining concepts are also presented here.

中文翻译:

云大数据挖掘和分析:在云中带来绿色和加速

自过去十年以来,大数据获得了极大的关注。几乎所有科学技术领域都从中受到了相当大的影响。云计算范例已针对大数据处理和挖掘,使用了从计算节点到高效存储的大量资源,从而可以更高效地进行处理。云数据挖掘引入了利用云资源对云中的大量数据进行数据挖掘和分析的概念。但是,我们可以做得更好吗?是的当然!本章的主要贡献是确定四种改变游戏规则的技术,以加速计算和分析云中的数据挖掘任务。图形处理单元可用于进一步加速挖掘或分析过程,这称为GPU加速分析。更多,近似计算还可以引入大数据分析中,以通过减少时间和精力并因此在整个计算过程中实现绿色来在过程中带来效率。量子计算是近来不断发展的范例,它也可以在极短的时间内促进高效,快速的大数据分析。我们已经对这三种技术进行了调查,并通过将这三个改变游戏规则的人与大数据的观点相结合,确立了它们在具有整体架构的大数据挖掘中的重要性。我们还讨论了另一种未来技术,即神经处理单元或神经加速器,供研究人员探索可能性。此处还简要介绍了大数据和云数据挖掘概念。
更新日期:2021-04-14
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