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Green Data Mining using Approximate Computing: An experimental analysis with Rule Mining

机译:使用近似计算的绿色数据挖掘:使用规则挖掘的实验分析

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Green Information and Communication Technology (G-ICT) is an emerging area of research and development and the major factor pertaining to this area is energy efficient designs for IT systems. Data mining deals with a special class of algorithms which are centered towards predicting and modeling patterns and trends in huge volume of digital data. Has anyone thought of the situation where data processing needs will surpass the energy production of the world? This is really going to happen if we don't start taking appropriate steps from now onwards. One of the most sought after solution is trading of accuracy of results with current consumption efficiency and good latency which is generally known as inexact or approximate computing. In this regard, we have applied some approximation techniques which can be used to achieve energy efficient data mining or Green Data mining with results as best as possible for a given allowable deviation. We have placed some experimental analysis on data mining algorithms to show the concept.
机译:绿色信息和通信技术(G-ICT)是新兴的研究与开发领域,与该领域相关的主要因素是IT系统的节能设计。数据挖掘处理一类特殊的算法,这些算法的重点是预测和建模大量数字数据中的模式和趋势。有谁想到过数据处理需求将超过世界能源生产的情况?如果我们从现在开始不采取适当的措施,这确实会发生。最受欢迎的解决方案之一是权衡结果的准确性与电流消耗效率和良好的延迟,这通常被称为不精确或近似计算。在这方面,我们应用了一些近似技术,这些技术可用于实现节能数据挖掘或绿色数据挖掘,并且对于给定的允许偏差,其结果应尽可能最佳。我们对数据挖掘算法进行了一些实验分析,以证明这一概念。

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