Mining Dynamics of Data Streams in Multi-Dimensional Space

流数据处理和挖掘代表了一类重要的新兴数据密集型应用程序，其中数据以巨大(可能是无限的)数据量动态地进出，仅适用于单扫描算法，但通常要求快速甚至实时的响应。基于大多数流数据存在于原始抽象级别，但最感兴趣的模式可能需要在多维空间的某些高抽象级别发现的观察，本项目专注于流数据挖掘问题，并开发有效、高效和可扩展的方法来挖掘多维空间中数据流的动态。这项研究的范围包括发现数据流中特征、聚类、分类模型和频繁模式的变化、趋势和演变。该方法是在简明的数据结构中捕获足够的统计、紧凑和聚集信息，以促进对连续和ad-doc流挖掘查询的有效处理。目前正在探索几个具有重要战略意义的应用，包括网络入侵检测、电信和Web数据流分析以及金融数据流分析。这一努力的成果将有助于开发实时数据挖掘系统的原理和新方法，并促进其具有重要战略意义的应用，包括及时发现恐怖主义或犯罪活动，用于国土安全、入侵检测、对数据密集型、快速变化的事件进行多维分析，以及其他具有广泛影响的领域。研究成果将及时发表在会议和期刊上(并可在http://www.cs.uiuc.edu/~hanj/projs/streamine.htm)上获得)，用于广泛传播、行业采用和对新一代信息技术学生和工作者的教育。

Stream data processing and mining represent an important, emerging class of data-intensive applications where data flows in and out dynamically, in huge (possibly infinite) volumes, adaptive to only single-scan algorithms, but often demanding fast or even real-time responses. Based on the observation that a majority of stream data resides at the primitive level of abstraction, but most interesting patterns may need to be discovered at certain high levels of abstraction in multi-dimensional space, this project focuses on the issues on stream data mining and develop effective, efficient and scalable methods for mining the dynamics of data streams in multi-dimensional space. The scope of this study includes the discovery of changes, trends, and evolutions of characteristics, clusters, classification models, and frequent patterns in data streams. The methodology is to capture sufficient statistical, compact and aggregate information in concise data structures to facilitate the efficient processing of both continuous and ad-doc stream mining queries. Several strategically important applications are being explored, including network intrusion detection, telecommunication and Web data flow analysis, and financial data flow analysis. This effort's results will contribute to the development of the principles and new methods for real-time data mining systems and promote its strategically important applications, including timely discovery of terrorist or criminal activities for homeland security, intrusion detection, multi-dimensional analysis of data-intensive, fast-changing events, and other areas with broad impact. The research results will be published timely in conferences and journals (and be made available at http://www.cs.uiuc.edu/~hanj/projs/streamine.htm) for wide dissemination, industry adoption, and education of new generation of information technology students and workers.