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Data Mining Techniques in Sensor Networks: Summarization, Interpolation and Surveillance (SpringerBriefs in Computer Science)
Book 87 of 322: SpringerBriefs in Computer ScienceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 47.99
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Condition: New. pp. 120.
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Data Mining Techniques in Sensor Networks: Summarization, Interpolation and Surveillance
Book 87 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer-Verlag New York Inc, 2013
ISBN 10: 1447154533 ISBN 13: 9781447154532
Paperback. Condition: Brand New. 2014 edition. 123 pages. 9.00x6.00x0.25 inches. In Stock.
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.
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Condition: New. Print on Demand pp. 120 39 Illus. (37 Col.).
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Condition: New. PRINT ON DEMAND pp. 120.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces the trend cluster, a recently defined spatio-temporal pattern, and its use in summarizing, interpolating and identifying anomalies in sensor networksIllustrates the application of trend cluster discovery to monitor the efficiency of pho.
