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FPGA-Based Stream Processing for Frequent Itemset Mining with Incremental Multiple Hashes

机译：基于FPGA的流处理，用于递增增量多个哈希的频繁项集挖掘

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摘要

With the advent of the IoT era, the amount of real-time data that is processed in data centers has increased explosively. As a result, stream mining, extracting useful knowledge from a huge amount of data in real time, is attracting more and more attention. It is said, however, that realtime stream processing will become more difficult in the near future, because the performance of processing applications continues to increase at a rate of 10% -15% each year, while the amount of data to be processed is increasing exponentially. In this study, we focused on identifying a promising stream mining algorithm, specifically a Frequent Itemset Mining (FIsM) algorithm, then we improved its performance using an FPGA. FIsM algorithms are important and are basic data-mining techniques used to discover association rules from transactional databases. We improved on an approximate FIsM algorithm proposed recently so that it would fit onto hardware architecture efficiently. We then ran experiments on an FPGA. As a result, we have been able to achieve a speed 400% faster than the original algorithm implemented on a CPU. Moreover, our FPGA prototype showed a 20 times speed improvement compared to the CPU version.
机译：随着物联网时代的到来，数据中心中处理的实时数据量呈爆炸性增长。结果，流挖掘实时地从大量数据中提取有用的知识，正引起越来越多的关注。但是，据说在不久的将来，实时流处理将变得更加困难，因为处理应用程序的性能以每年10％-15％的速度持续增长，而要处理的数据量却在不断增加。指数增长。在这项研究中，我们专注于确定一种很有前途的流挖掘算法，特别是频繁项集挖掘（FIsM）算法，然后我们使用FPGA来提高其性能。 FIsM算法很重要，并且是用于从事务数据库中发现关联规则的基本数据挖掘技术。我们对最近提出的近似FIsM算法进行了改进，使其可以有效地适合硬件架构。然后，我们在FPGA上进行了实验。结果，我们能够比在CPU上实现的原始算法快400％的速度。而且，我们的FPGA原型与CPU版本相比，速度提高了20倍。

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