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首页> 外文期刊>International Journal of Innovative Computing Information and Control >AN EFFICIENT ONE-PASS METHOD FOR DISCOVERING BASES OF RECENTLY FREQUENT EPISODES OVER ONLINE DATA STREAMS
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AN EFFICIENT ONE-PASS METHOD FOR DISCOVERING BASES OF RECENTLY FREQUENT EPISODES OVER ONLINE DATA STREAMS

机译：一种通过在线数据流发现最近频率基点的有效单通方法

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摘要

The knowledge embedded in an online data stream is likely to change over time due to the dynamic evolution of the stream. Consequently, in frequent episode mining over an online stream, frequent episodes should be adoptively extracted from recently generated stream segments instead of the whole stream. However, almost all existing frequent episode mining approaches find episodes frequently occurring over the whole sequence. This paper proposes and investigates a new problem: online mining of recently frequent episodes over data streams. In order to meet strict requirements of stream mining such as one-scan, adaptive result update and instant result return, we choose a novel frequency metric and define a highly condensed set called the base of recently frequent episodes. We then introduce a one-pass method for mining bases of recently frequent episodes. Experimental results show that the proposed method is capable of finding bases of recently frequent episodes quickly and adaptively. The proposed method outperforms the previous approaches with the advantages of one-pass, instant result update and return, more condensed resulting sets and less space usage.
机译：由于数据流的动态演变，在线数据流中嵌入的知识可能会随时间而变化。因此，在在线流上的频繁情节挖掘中，应该从最近生成的流分段而不是整个流中过继地提取频繁情节。但是，几乎所有现有的频繁情节挖掘方法都发现在整个序列中频繁发生的情节。本文提出并研究了一个新问题：在线挖掘数据流中最近出现的事件。为了满足流挖掘的严格要求，例如一次扫描，自适应结果更新和即时结果返回，我们选择一种新颖的频率度量并定义一个高度压缩的集合，称为最近频繁发生的事件的基础。然后，我们介绍一种用于通过挖掘最近频繁发生的事件的基地的方法。实验结果表明，该方法能够快速，自适应地找到最近发生事件的基础。所提出的方法具有以下优点：一次通过，即时结果更新和返回，更紧凑的结果集以及更少的空间使用。

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