000			03850nam a22006135i 4500
001			978-3-642-12519-5
003			DE-He213
005			20140220084535.0
007			cr nn 008mamaa
008			100407s2010 gw | s |||| 0|eng d
020			_a9783642125195 _9978-3-642-12519-5
024	7		_a10.1007/978-3-642-12519-5 _2doi
050		4	_aQA75.5-76.95
072		7	_aUNH _2bicssc
072		7	_aUND _2bicssc
072		7	_aCOM030000 _2bisacsh
082	0	4	_a025.04 _223
100	1		_aGaber, Mohamed Medhat. _eeditor.
245	1	0	_aKnowledge Discovery from Sensor Data _h[electronic resource] : _bSecond International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers / _cedited by Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Auroop R. Ganguly.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aIX, 227p. 110 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x0302-9743 ; _v5840
505	0		_aData Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned -- Monitoring Incremental Histogram Distribution for Change Detection in Data Streams -- Situation-Aware Adaptive Visualization for Sensory Data Stream Mining -- Unsupervised Plan Detection with Factor Graphs -- WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System -- Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set -- Spatio-temporal Outlier Detection in Precipitation Data -- Large-Scale Inference of Network-Service Disruption upon Natural Disasters -- An Adaptive Sensor Mining Framework for Pervasive Computing Applications -- A Simple Dense Pixel Visualization for Mobile Sensor Data Mining -- Incremental Anomaly Detection Approach for Characterizing Unusual Profiles -- Spatiotemporal Neighborhood Discovery for Sensor Data.
520			_aThis book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.
650		0	_aComputer science.
650		0	_aComputer Communication Networks.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aInformation storage and retrieval systems.
650		0	_aOptical pattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aInformation Storage and Retrieval.
650	2	4	_aComputer Communication Networks.
650	2	4	_aDatabase Management.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aPattern Recognition.
700	1		_aVatsavai, Ranga Raju. _eeditor.
700	1		_aOmitaomu, Olufemi A. _eeditor.
700	1		_aGama, João. _eeditor.
700	1		_aChawla, Nitesh V. _eeditor.
700	1		_aGanguly, Auroop R. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642125188
830		0	_aLecture Notes in Computer Science, _x0302-9743 ; _v5840
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-12519-5
912			_aZDB-2-SCS
912			_aZDB-2-LNC
999			_c112092 _d112092