Finding periodic outliers over a monogenetic event stream

Kimio Kuramitsu
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引用次数: 5

Abstract

Sensors are active everywhere. Enormous volumes of sensed events are sent over the data streams, while most of applications want to focus on events that would be curious. We propose a technique for mining periodicities and predicting its outliers from the stream. The key to our technique is a simple periodic pattern /spl Delta/t, derived from delta-time mining, or SUP(t, t+/spl Delta/t). We provide efficient algorithms for finding the highest support /spl Delta/t on a small and resource-limited sensor device. Our experiments compare memory efficiency and accuracy, on a variety of event patterns, monogenesis, polygenesis, and semi-random.
在单基因事件流中发现周期性异常值
传感器无处不在。大量的感知事件通过数据流发送,而大多数应用程序都希望关注那些令人好奇的事件。我们提出了一种从流中挖掘周期并预测其异常值的技术。我们技术的关键是一个简单的周期模式/spl Delta/t,源自于增量时间挖掘,或SUP(t, t+/spl Delta/t)。我们提供了在小型和资源有限的传感器设备上找到最高支持/spl Delta/t的有效算法。我们的实验比较了记忆效率和准确性,在各种事件模式,单发生,多发生和半随机。
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