Learning from Data Streams

Processing Techniques in Sensor Networks

  • João Gama
  • Mohamed Medhat Gaber

Table of contents

  1. Front Matter
    Pages I-X
  2. Overview

    1. Front Matter
      Pages 7-7
    2. João Gama, Mohamed Medhat Gaber
      Pages 1-5
    3. João Barros
      Pages 9-24
    4. João Gama, Pedro Pereira Rodrigues
      Pages 25-39
    5. Mohamed Medhat Gaber
      Pages 41-48
  3. Data Stream Management Techniques in Sensor Networks

    1. Front Matter
      Pages 49-49
    2. M. A. Hammad, T. M. Ghanem, W. G. Aref, A. K. Elmagarmid, M. F. Mokbel
      Pages 51-71
    3. Niki Trigoni, Alexandre Guitton, Antonios Skordylis
      Pages 73-86
    4. Nisheeth Shrivastava, Chiranjeeb Buragohain
      Pages 87-105
    5. Rachel Cardell-Oliver
      Pages 107-122
  4. Mining Sensor Network Data Streams

    1. Front Matter
      Pages 123-123
    2. Pedro Pereira Rodrigues, João Gama
      Pages 125-142
    3. João Gama, Rasmus Ulslev Pedersen
      Pages 143-164
    4. Jimeng Sun, Spiros Papadimitriou, Philip S. Yu
      Pages 165-184
  5. Applications

    1. Front Matter
      Pages 185-185
    2. Auroop R. Ganguly, Olufemi A. Omitaomu, Randy M. Walker
      Pages 187-204
    3. Auroop R. Ganguly, Olufemi A. Omitaomu, Yi Fang, Shiraj Khan, Budhendra L. Bhaduri
      Pages 205-229
    4. Rasmus Ulslev Pedersen
      Pages 231-241
  6. Back Matter
    Pages 243-244

About this book

Introduction

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.

Keywords

LEGO Mindstorms Mindstorms Predictive Learning Sensor Data Sensor Networks TinyOS architectures clustering information knowledge discovery learning memory monitor wireless sensor network wireless sensor networks

Editors and affiliations

  • João Gama
    • 1
  • Mohamed Medhat Gaber
    • 2
  1. 1.Laboratory of Artificial Intelligence and Decision Support, INESC-Porto LA and Faculty of EconomicsUniversity of PortoPortoPortugal
  2. 2.Tasmanian ICT CentreHobartAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-73679-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-73678-3
  • Online ISBN 978-3-540-73679-0
  • About this book