大数据时代流数据的变化检测:模型与问题

Dang-Hoan Tran, Mohamed Medhat Gaber, K. Sattler
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引用次数: 27

摘要

大数据有三个v,即速度(velocity)、数量(volume)和种类(variety)。数据流处理领域长期以来一直在处理前两个v——速度和体积。经过十多年的深入研究,该社区在该领域提供了许多重要的研究发现。大数据的第三个V是社交媒体及其产生的大量非结构化数据的结果。最近也提出了流媒体技术来解决这个新出现的需求。然而,一个隐藏的因素可以代表重要的第四个V,即可变性或变化。我们的世界正在迅速变化,考虑到可变性是一个关键的成功因素。本文概述了应用于流数据的变更检测技术。随着大数据技术的兴起,这篇综述是及时的,有必要强调这一重要方面,并对其技术进行分类和详细说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Change detection in streaming data in the era of big data: models and issues
Big Data is identified by its three Vs, namely velocity, volume, and variety. The area of data stream processing has long dealt with the former two Vs velocity and volume. Over a decade of intensive research, the community has provided many important research discoveries in the area. The third V of Big Data has been the result of social media and the large unstructured data it generates. Streaming techniques have also been proposed recently addressing this emerging need. However, a hidden factor can represent an important fourth V, that is variability or change. Our world is changing rapidly, and accounting to variability is a crucial success factor. This paper provides a survey of change detection techniques as applied to streaming data. The review is timely with the rise of Big Data technologies, and the need to have this important aspect highlighted and its techniques categorized and detailed.
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