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，其中数据以巨大（可能无限）的量动态流入和流出，仅适用于单扫描算法，但通常要求快速甚至实时响应。基于大多数流数据都存在于原始的抽象层次，而大多数有趣的模式可能需要在多维空间的某些高抽象层次上发现的观察，本项目重点研究流数据挖掘问题，并开发有效的，高效的和可扩展的方法来挖掘多维空间中的数据流动态。本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。该方法是捕捉足够的统计，紧凑和聚合信息简洁的数据结构，以促进连续和ad-doc流挖掘查询的有效处理。目前正在探索若干具有重要战略意义的应用，包括网络入侵检测、电信和网络数据流分析以及金融数据流分析。这项工作的结果将有助于实时数据挖掘系统的原则和新方法的发展，并促进其具有重要战略意义的应用，包括及时发现国土安全的恐怖主义或犯罪活动，入侵检测，数据密集型的多维分析，快速变化的事件以及其他具有广泛影响的领域。研究结果将及时在会议和期刊上发表（并可在http：//www.cs.uiuc.edu/cnhanj/projs/streamine.htm上查阅），以便广泛传播、行业采用和教育新一代的信息技术学生和工作者。

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.