Data Mining Techniques in Sensor Networks - Summarization, Interpolation and Surveillance (Paperback, 2014 ed.)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	SpringerBriefs in Computer Science
Release date:	September 2013
First published:	2014
Authors:	Annalisa Appice • Anna Ciampi • Fabio Fumarola • Donato Malerba
Dimensions:	235 x 155 x 11mm (L x W x T)
Format:	Paperback
Pages:	105
Edition:	2014 ed.
ISBN-13:	978-1-4471-5453-2
Categories:	Books > Computing & IT > Internet > Network computers Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	1-4471-5453-3
Barcode:	9781447154532

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!