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Knowledge Discovery from Sensor Data [electronic resource] : Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers / edited by Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Auroop R. Ganguly.

Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science ; 5840Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: IX, 227p. 110 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642125195
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 025.04 23
LOC classification:
  • QA75.5-76.95
Online resources:
Contents:
Data 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.
In: Springer eBooks
Item type: E-BOOKS
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IMSc Library Link to resource Available EBK9003

Data 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.

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The Institute of Mathematical Sciences, Chennai, India