Stream Data mining in telecommunication industry with privacy preserving consideration

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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.