Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
Hrishav Bakul Barua, Kartick Chandra Mondal

TL;DR
This paper reviews four innovative technologies—GPU acceleration, approximate computing, quantum computing, and neural accelerators—that can significantly enhance the efficiency and greenness of big data mining in cloud environments.
Contribution
It identifies and surveys four key technologies that can accelerate and make big data analytics more energy-efficient in cloud computing.
Findings
GPU acceleration enhances data mining speed.
Approximate computing reduces energy consumption.
Quantum computing offers rapid analytics capabilities.
Abstract
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…
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Taxonomy
TopicsCloud Computing and Resource Management · Neural Networks and Applications · Stochastic Gradient Optimization Techniques
