Learning from Data Streams
Processing Techniques in Sensor Networks
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Beschreibung
Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate.
The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.
This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research.
Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-the-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education. This research monograph delivers to researchers and graduate students the state-of-the-art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research.
Shows how to apply machine learning techniques to stream data processing Details data stream mining approaches using clustering, predictive learning, and tensor analysis techniques Presents applications in security, the natural sciences, and education Includes descriptions of famous prototype implementations like the Nile system and the TinyOS operating system Includes supplementary material: sn.pub/extras
Autor*in
João Gama
Themen in »Learning from Data Streams«
LEGO Mindstorms Mindstorms Predictive Learning Sensor Data Sensor Networks TinyOS architectures clustering information knowledge discovery learning memory monitor wireless sensor network wireless sensor networks