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Study of High-speed Data Mining Algorithms from Massive Data Streams

海量数据流高速数据挖掘算法研究

基本信息

批准号：
15300036
负责人：
IKEDA Daisuke
金额：
$ 9.98万
依托单位：
KYUSHU UNIVERSITY (2003, 2005)Hokkaido University (2004)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

In this research, we investigated high-speed online knowledge discovery system for extracting useful information from massive semi-structured data streams. Particularly in this year, as theoretical researches, we extended further the theory of efficient pattern matching and pattern discovery methods for online streams. As application studies, we made a series of experiments on collection and analysis of network data from real high-speed networks in a huge organization. We have also published the results obtained in the research period of the last three years. In particular, we proceed the studies on the following issues:(1)Survey on semi-structured data : We have summarized and published a survey on stream data mining in an academic journal, which has been studied through this project for the last three years.(2)Study on streaming pattern matching technology for semi-structured data : We developed an efficient method for performing tree pattern matching with horizontal wildcards by bit parallel technology, which potentially gives drastic speed-up for Xpath and XQuery pattern matching languages for huge XML data.(3)Study on sequential and streaming pattern discovery technology for semi-structured data : We developed efficient algorithms for finding interesting patterns from massive data streams for various classes of complex patterns/motifs. In this year, we also published pattern discovery algorithms developed in the last year. Also, one of them got awarded for 2004 JSAI SIG AWARD.(4)Empirical study on knowledge discovery from real massive network data : As applications, we performed a series of surveys on data collection and online analysis of high-speed large-scale network for middle sized organization at Kyushu University. These experiments will give insights for future research on the development of efficient pattern matching/discovery algorithms for high-speed streaming data.

在本研究中，我们研究了高速在线知识发现系统，从大量的半结构化数据流中提取有用的信息。特别是在今年，作为理论研究，我们进一步扩展了在线流的有效模式匹配和模式发现方法的理论。作为应用研究，我们在一个大型组织中对真实的高速网络进行了一系列的网络数据采集和分析实验。我们还公布了过去三年研究期间取得的成果。（1）半结构化数据综述：我们总结并在学术期刊上发表了一份关于流数据挖掘的综述，该综述是本项目近三年来的研究成果。（2）半结构化数据流模式匹配技术的研究：提出了一种基于位并行技术的水平通配符树模式匹配方法，该方法可以大大提高XPath和XQuery模式匹配语言在处理海量XML数据时的速度。（3）半结构化数据的顺序和流式模式发现技术研究：针对各类复杂模式/基元，提出了从海量数据流中发现感兴趣模式的有效算法。今年，我们还发表了去年开发的模式发现算法。此外，其中一人获得了2004年JSAI SIG奖。（4）从真实的海量网络数据中进行知识发现的实证研究：作为应用，我们在九州大学进行了一系列针对中型组织的高速大规模网络数据收集和在线分析的调查。这些实验将为未来的研究提供见解，为高速流数据的高效模式匹配/发现算法的发展。