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Data Mining Techniques in Sensor Networks

Summarization, Interpolation and Surveillance

  • Book
  • © 2014

Overview

Authors:
  1. Annalisa Appice

    1. Dipartimento di Informatica, Università degli Studi di Bari "Aldo Moro", Italy

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  2. Anna Ciampi

    1. Dipartimento di Informatica, Università degli Studi di Bari "Aldo Moro", Italy

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  3. Fabio Fumarola

    1. Dipartimento di Informatica, Università degli Studi di Bari "Aldo Moro", Italy

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  4. Donato Malerba

    1. Dipartimento di Informatica, Università degli Studi di Bari "Aldo Moro", Italy

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  • Introduces the trend cluster, a recently defined spatio-temporal pattern, and its use in summarizing, interpolating and identifying anomalies in sensor networks
  • Illustrates the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants
  • Discusses new possibilities for surveillance enabled by recent developments in sensing technology

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Sensor Networks and Data Streams: Basics

    • Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba
    Pages 1-8

  3. Geodata Stream Summarization

    • Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba
    Pages 9-48

  4. Missing Sensor Data Interpolation

    • Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba
    Pages 49-71

  5. Sensor Data Surveillance

    • Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba
    Pages 73-88

  6. Sensor Data Analysis Applications

    • Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba
    Pages 89-102

  7. Back Matter

    Pages 103-105
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Keywords

About this book

Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data. This work introduces a recently defined spatio-temporal pattern, called trend cluster, to summarize, interpolate and identify anomalies in a sensor network. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.

Authors and Affiliations

  • Dipartimento di Informatica, Università degli Studi di Bari "Aldo Moro", Italy

    Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba

Bibliographic Information

  • Book Title: Data Mining Techniques in Sensor Networks

  • Book Subtitle: Summarization, Interpolation and Surveillance

  • Authors: Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba

  • Series Title: SpringerBriefs in Computer Science

  • DOI: https://doi.org/10.1007/978-1-4471-5454-9

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Author(s) 2014

  • Softcover ISBN: 978-1-4471-5453-2Published: 27 September 2013

  • eBook ISBN: 978-1-4471-5454-9Published: 12 September 2013

  • Series ISSN: 2191-5768

  • Series E-ISSN: 2191-5776

  • Edition Number: 1

  • Number of Pages: XIII, 105

  • Number of Illustrations: 2 b/w illustrations, 37 illustrations in colour

  • Topics: Data Mining and Knowledge Discovery, Computer Communication Networks

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