Online mining of data streams: applications, techniques and progress

Haixun Wang, J. Pei, Philip S. Yu
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引用次数: 10

Abstract

In this paper, we focus on the differences between mining static large data sets and data streams. Over the years, the database and data mining community have learned valuable lessons from mining static large data sets, and developed many useful algorithms and tools for this purpose. The paper aims at providing a shortcut to the current frontier of stream mining research. We emphasize the research problems, the inherent technical challenges and the latest results. Particularly, the paper highlights new challenges and potential research interests. Research community has been interested in the integration between data mining tasks and database management systems.
数据流的在线挖掘:应用、技术和进展
在本文中,我们着重于挖掘静态大数据集和数据流之间的区别。多年来,数据库和数据挖掘社区从挖掘静态大型数据集中学到了宝贵的经验,并为此开发了许多有用的算法和工具。本文旨在为当前河流开采研究的前沿提供一条捷径。我们强调研究问题,固有的技术挑战和最新成果。特别指出了新的挑战和潜在的研究方向。数据挖掘任务与数据库管理系统之间的集成一直是研究界关注的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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