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Mining@home: toward a public-resource computing framework for distributed data mining

机译:Mining @ home:面向用于分布式数据挖掘的公共资源计算框架

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Several classes of scientific and commercial applications require the execution of a large number of independent tasks. One highly successful and low-cost mechanism for acquiring the necessary computing power for these applications is the 'public-resource computing', or 'desktop Grid' paradigm, which exploits the computational power of private computers. So far, this paradigm has not been applied to data mining applications for two main reasons. First, it is not straightforward to decompose a data mining algorithm into truly independent sub-tasks. Second, the large volume of the involved data makes it difficult to handle the communication costs of a parallel paradigm. This paper introduces a general framework for distributed data mining applications called Mining@home. In particular, we focus on one of the main data mining problems: the extraction of closed frequent itemsets from transactional databases. We show that it is possible to decompose this problem into independent tasks, which however need to share a large volume of the data. We thus introduce a data-intensive computing network, which adopts a P2P topology based on super peers with caching capabilities, aiming to support the dissemination of large amounts of information. Finally, we evaluate the execution of a pattern extraction task on such network.
机译:几类科学和商业应用程序需要执行大量独立的任务。一种用于获取这些应用程序所需的计算能力的,非常成功且低成本的机制是“公共资源计算”或“桌面网格”范式,它利用了私有计算机的计算能力。到目前为止,由于两个主要原因,该范例尚未应用于数据挖掘应用程序。首先,将数据挖掘算法分解为真正独立的子任务并不容易。其次,所涉及的数据量很大,很难处理并行范例的通信成本。本文介绍了一个名为Mining @ home的分布式数据挖掘应用程序的通用框架。特别是,我们专注于主要的数据挖掘问题之一:从事务数据库中提取封闭的频繁项目集。我们表明有可能将这个问题分解为独立的任务,但是这些任务需要共享大量数据。因此,我们引入了一种数据密集型计算网络,该网络采用基于具有缓存功能的超级对等点的P2P拓扑,旨在支持大量信息的分发。最后,我们评估了这种网络上模式提取任务的执行情况。

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