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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining
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Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining

机译：一种频繁模式挖掘可重构平台的设计与分析

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摘要

Frequent pattern mining algorithms are designed to find commonly occurring sets in databases. This class of algorithms is typically very memory intensive, leading to prohibitive runtimes on large databases. A class of reconfigurable architectures has been recently developed that have shown promise in accelerating some data mining applications. In this paper, we propose a new architecture for frequent pattern mining based on a systolic tree structure. The goal of this architecture is to mimic the internal memory layout of the original pattern mining software algorithm while achieving a higher throughput. We provide a detailed analysis of the area and performance requirements of our systolic tree-based architecture, and show that our reconfigurable platform is faster than the original software algorithm for mining long frequent patterns.
机译：频繁模式挖掘算法旨在查找数据库中常见的集合。这类算法通常占用大量内存，从而导致大型数据库无法正常运行。最近开发了一类可重新配置的体系结构，它们在加速某些数据挖掘应用程序方面显示出了希望。在本文中，我们提出了一种基于收缩树结构的频繁模式挖掘新架构。该体系结构的目标是模仿原始模式挖掘软件算法的内部存储器布局，同时实现更高的吞吐量。我们对基于收缩树的体系结构的面积和性能要求进行了详细分析，并表明我们的可重配置平台比用于挖掘长期频繁模式的原始软件算法要快。

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