Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Sel by Mohamed Medhat GaberKnowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Sel by Mohamed Medhat Gaber

Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV…

byMohamed Medhat GaberEditorRanga Raju Vatsavai, Olufemi A. Omitaomu

Paperback | April 14, 2010

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This 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.
Title:Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV…Format:PaperbackDimensions:227 pages, 23.5 × 15.5 × 0.02 inPublished:April 14, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3642125182

ISBN - 13:9783642125188

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Table of 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.