000			03727nam a22005055i 4500
001			978-1-4471-5454-9
003			DE-He213
005			20140220082456.0
007			cr nn 008mamaa
008			130912s2014 xxk| s |||| 0|eng d
020			_a9781447154549 _9978-1-4471-5454-9
024	7		_a10.1007/978-1-4471-5454-9 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aAppice, Annalisa. _eauthor.
245	1	0	_aData Mining Techniques in Sensor Networks _h[electronic resource] : _bSummarization, Interpolation and Surveillance / _cby Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2014.
300			_aXIII, 105 p. 39 illus., 37 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
505	0		_aIntroduction -- Sensor Networks and Data Streams: Basics -- Geodata Stream Summarization -- Missing Sensor Data Interpolation -- Sensor Data Surveillance -- Sensor Data Analysis Applications.
520			_aEmerging real life applications, such as environmental compliance, ecological studies and meteorology, are characterized by real-time data acquisition through a number of (wireless) remote sensors. Operatively, remote sensors are installed across a spatially distributed network; they gather information along a number of attribute dimensions and periodically feed a central server with the measured data. The server is required to monitor these data, issue possible alarms or compute fast aggregates. As data analysis requests, which are submitted to a server, may concern both present and past data, the server is forced to store the entire stream. But, in the case of massive streams (large networks and/or frequent transmissions), the limited storage capacity of a server may impose to reduce the amount of data stored on the disk.  One solution to address the storage limits is to compute summaries of the data as they arrive and use these summaries to interpolate the real data which are discarded instead.  On any future demands of further analysis of the discarded data, the server pieces together the data from the summaries stored in database and processes them according to the requests. This work introduces the multiple possibilities and facets of a recently defined spatio-temporal pattern, called trend cluster, and its applications to summarize, interpolate and identify anomalies in a sensor network.   As an example application, the authors illustrate the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants. The work closes with remarks on new possibilities for surveillance gained by recent developments of sensing technology, and with an outline of future challenges.
650		0	_aComputer science.
650		0	_aComputer Communication Networks.
650		0	_aData mining.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aComputer Communication Networks.
700	1		_aCiampi, Anna. _eauthor.
700	1		_aFumarola, Fabio. _eauthor.
700	1		_aMalerba, Donato. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447154532
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-5454-9
912			_aZDB-2-SCS
999			_c91889 _d91889