Editors:
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
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Table of contents (14 chapters)
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Front Matter
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Data Stream Management Techniques in Sensor Networks
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Front Matter
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Mining Sensor Network Data Streams
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Front Matter
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Back Matter
About this book
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.
Editors and Affiliations
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Laboratory of Artificial Intelligence and Decision Support, INESC-Porto LA and Faculty of Economics, University of Porto, Porto, Portugal
João Gama
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Tasmanian ICT Centre, Hobart, Australia
Mohamed Medhat Gaber
Bibliographic Information
Book Title: Learning from Data Streams
Book Subtitle: Processing Techniques in Sensor Networks
Editors: João Gama, Mohamed Medhat Gaber
DOI: https://doi.org/10.1007/3-540-73679-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
Hardcover ISBN: 978-3-540-73678-3Published: 11 October 2007
Softcover ISBN: 978-3-642-09285-5Published: 19 October 2010
eBook ISBN: 978-3-540-73679-0Published: 20 September 2007
Edition Number: 1
Number of Pages: X, 244
Number of Illustrations: 73 b/w illustrations
Topics: Information Storage and Retrieval, Computer Communication Networks, Signal, Image and Speech Processing, Communications Engineering, Networks, Artificial Intelligence