Learning from Data Streams

by Gama, Joao; Gaber, Mohamed Medhat

• ISBN13: 9783540736783
• ISBN10: 3540736786
• Format: Hardcover
• Copyright: 2007-11-03
• Publisher: Springer-Nature New York Inc

Summary

Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process information and communicate with neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge amounts of data streams to be managed continuously and at a very high rate. Gama and Gaber provide the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system or the TinyOS operating system. They detail data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and apply them to applications in security, natural sciences, and education. Their research monograph delivers to researchers and graduate students the state of the art in mining data streams. The huge list of literature referenced offers an excellent starting point for further reading and future research.

Table of Contents

Introduction	p. 1
Overview
Sensor Networks: An Overview	p. 9
Data Stream Processing	p. 25
Data Stream Processing in Sensor Networks	p. 41
Data Stream Management Techniques in Sensor Networks
Data Stream Management Systems and Architectures	p. 51
Querying of Sensor Data	p. 73
Aggregation and Summarization in Sensor Networks	p. 87
Sensory Data Monitoring	p. 107
Mining Sensor Network Data Streams
Clustering Techniques in Sensor Networks	p. 125
Predictive Learning in Sensor Networks	p. 143
Tensor Analysis on Multi-aspect Streams	p. 165
Applications
Knowledge Discovery from Sensor Data for Security Applications	p. 187
Knowledge Discovery from Sensor Data For Scientific Applications	p. 205
TinyOS Education with LEGO MINDSTORMS NXT	p. 231
Index	p. 243
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