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Mining Data - Streams

Mining Data - Streams
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Author(s): Hanady Abdulsalam (Queen’s University, Canada), David B. Skillicorn (Queen’s University, Canada) and Pat Martin (Queen’s University, Canada)
Copyright: 2008
Pages: 23
Source title: Successes and New Directions in Data Mining
Source Author(s)/Editor(s): Pascal Poncelet (Ecole des Mines d'Ales, France), Florent Masseglia (Project AxIS-INRIA, France) and Maguelonne Teisseire (Universite Montpellier, France)
DOI: 10.4018/978-1-59904-645-7.ch013

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Abstract

Data analysis or data mining have been applied to data produced by many kinds of systems. Some systems, for example road traffic monitoring, produce data continuously, and often at high rates. Analyzing such data creates new issues, because it is neither appropriate, nor perhaps possible, to accumulate it and process it using standard data-mining techniques. The information implicit in each data record must be extracted in a limited amount of time and, usually, without the possibility of going back to consider it again. Existing algorithms must be modified to apply in this new setting. This chapter outlines and analyzes the most recent research work in the area of data-stream mining. It gives some sample research ideas or algorithms in this field and concludes with a comparison that shows the main advantages and disadvantages of the algorithms. It also includes a discussion and possible future work in the area.

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