Learning from Data Streams : Processing Techniques in Sensor Networks

Gama, Joao; Gaber, Mohamed Medhat (EDT)

ISBN 10: 3642092853 / ISBN 13: 9783642092855

Published by Springer, 2010

About this Item

Seller Inventory # 11984948-n

Bibliographic Details

• Title: Learning from Data Streams : Processing Techniques in Sensor Networks
• Publisher: Springer
• Publication Date: 2010
• Language: English
• ISBN 10: 3642092853
• ISBN 13: 9783642092855
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

From the Back Cover

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.