Stream data analysis
Stream mining is the process of discovering knowledge or patterns from continuous data streams. Unlike traditional data sets, data streams consist of sequences of data instances that flow in and out of a system continuously and with varying update rates. They are temporally ordered, fast changing, massive, and potentially infinite. Examples of data streams include data generated by communication networks, Internet traffic, online stock or business transactions, electric power grids, industry production processes, scientific and engineering experiments, and video, audio or remote sensing data from cameras, satellites, and sensor networks. Since it is usually impossible to store an entire data stream, or to scan through it multiple times due to its tremendous volume, most stream mining algorithms are confined to reading only once or a small number of times using limited computing and storage capabilities. Moreover, much of stream da ...
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- Stream Mining
- Reference Work Title
- Encyclopedia of Database Systems
- pp 2831-2834
- Print ISBN
- Online ISBN
- Springer US
- Copyright Holder
- Springer US
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- Editor Affiliations
- 1. College of Computing, Georgia Institute of Technology
- 2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo
- Author Affiliations
- 1. University of Illinois at Urbana-Champaign, Champaign, IL, USA
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