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Running Data Mining Applications on the Grid: A Bag-of-Tasks Approach

机译:在网格上运行数据挖掘应用程序:任务袋方法

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摘要

Data mining (DM) applications are composed of computing-intensive processing tasks working on huge datasets. Due to its computing-intensive nature, these applications are natural candidates for execution on high performance, high throughput platforms such as PC clusters and computational grids. Many data mining algorithms can be implemented as bag-of-tasks (BoT) applications, i.e., parallel applications composed of independent tasks. This paper discusses the use of computing grids for the execution of DM algorithms as BoT applications, investigates the scalability of the execution of an application and proposes an approach to improve its scalability.
机译:数据挖掘(DM)应用程序由处理庞大数据集的计算密集型处理任务组成。由于其计算密集型的性质,这些应用程序自然可以在高性能,高吞吐量的平台(例如PC群集和计算网格)上执行。许多数据挖掘算法可以实现为任务袋(BoT)应用程序,即由独立任务组成的并行应用程序。本文讨论了在BoT应用程序中使用计算网格执行DM算法的方法,研究了应用程序执行的可伸缩性,并提出了一种提高其可伸缩性的方法。

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