Stream Data mining in telecommunication industry with privacy preserving consideration

知识发现和数据挖掘是从海量数据中提取隐含的、新颖的和有用的信息的重要过程。它是统计学、数据管理、计算机编程、机器学习和人工智能等多个科学技术领域的独特结合。流数据挖掘代表了一类重要的数据密集型应用程序，其中信息包以大容量动态流动，通常需要快速和实时地处理最新的信息。流数据挖掘的应用包括Web数据流分析、股市分析和网络安全，目前已有的许多数据挖掘算法在静态数据上表现良好，但在分析包含海量数据的数据流和流数据的时态特征方面存在不足。与存储数据集的数据处理方法不同，分析流数据的解决方案需要快速和内存高效的技术。本研究的重点是探索挖掘数据流的新方法。该研究的目的是发现和了解流数据挖掘中的各种挑战，并对流数据挖掘技术的原理、算法和应用进行系统的研究。此外，由于数据挖掘技术有助于发现可能是保密的和受法律保护的隐藏模式，我们将考虑数据流挖掘中的隐私保护问题，并开发一个框架来比较各种隐私保护技术。研究结果将用于开发高效和可扩展的数据流挖掘方法，并将其应用于电信行业的具体应用。

Knowledge Discovery and Data mining is the nontrivial process of extracting implicit, novel, and useful information from large volume of data. It has emerged as a unique combination of several fields of science and technology including statistics, data management, computer programming, machine learning, and artificial intelligence.Stream data mining represents an important class of data-intensive applications where packets of information flow dynamically in large volumes, often demanding fast and real-time processing of the most current information. Applications of stream data mining include web data flow analysis, stock market analysis, and network security.Many of the currently established data mining algorithms perform well on static data yet fall short of addressing the challenges of analyzing data streams including high volumes of data, and temporal characteristics of streaming data. Unlike data processing methods for stored datasets, solutions for analyzing streaming data require fast and memory efficient techniques.The focus of this research is to explore novel methodologies for mining streams of data.  The research is aimed at discovering and understanding the various challenges in stream data mining, and to perform a systematic investigation of the principles, algorithms, and applications of stream data mining techniques. Additionally, since data mining techniques facilitate discovery of hidden patterns which might be confidential and legally protected, we will consider privacy preserving issues in mining streams of data, and develop a framework to compare various privacy preserving techniques.The results of the research will be used to develop efficient and scalable methods for mining streams of data, and subsequently applying them to specific applications in telecommunications industry.