Real-Time Data Mining for Event Streams

Massiva Roudjane, D. Rebaine, R. Khoury, Sylvain Hallé
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引用次数: 6

Abstract

Information systems produce different types of event logs; in many situations, it may be desirable to look for trends inside these logs. We show how trends of various kinds can be computed over such logs in real time, using a generic framework called the trend distance workflow. Many common computations on event streams turn out to be special cases of this workflow, depending on how a handful of workflow parameters are defined. This process has been implemented and tested in a real-world event stream processing tool, called BeepBeep. Experimental results show that deviations from a reference trend can be detected in realtime for streams producing up to thousands of events per second.
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事件流的实时数据挖掘
信息系统产生不同类型的事件日志;在许多情况下,可能需要在这些日志中查找趋势。我们展示了如何使用一种称为趋势距离工作流的通用框架,在这些日志上实时计算各种趋势。事件流上的许多常见计算结果是此工作流的特殊情况,这取决于如何定义少量工作流参数。这个过程已经在一个叫做BeepBeep的真实事件流处理工具中实现和测试过。实验结果表明,对于每秒产生数千个事件的流,可以实时检测到与参考趋势的偏差。
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