Titlebook: Knowledge Discovery from Sensor Data; Second International Mohamed Medhat Gaber,Ranga Raju Vatsavai,Auroop R. Conference proceedings 2010 S

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書目名稱Knowledge Discovery from Sensor Data影響因子(影響力)




書目名稱Knowledge Discovery from Sensor Data影響因子(影響力)學(xué)科排名




書目名稱Knowledge Discovery from Sensor Data網(wǎng)絡(luò)公開度




書目名稱Knowledge Discovery from Sensor Data網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Knowledge Discovery from Sensor Data被引頻次




書目名稱Knowledge Discovery from Sensor Data被引頻次學(xué)科排名




書目名稱Knowledge Discovery from Sensor Data年度引用




書目名稱Knowledge Discovery from Sensor Data年度引用學(xué)科排名




書目名稱Knowledge Discovery from Sensor Data讀者反饋




書目名稱Knowledge Discovery from Sensor Data讀者反饋學(xué)科排名

Maximize

Spatiotemporal Neighborhood Discovery for Sensor Data, discretize temporal intervals. These methods were tested on real life datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b)highway sensor network data archive. We have found encouraging results which are validated by real life phenomenon.

發(fā)牢騷

Unsupervised Plan Detection with Factor Graphs,levant locations. Instead, we introduce 2 unsupervised methods to simultaneously estimate model parameters and hidden values within a Factor graph representing agent transitions over time. We evaluate our approach by applying it to goal prediction in a GPS dataset tracking 1074 ships over 5 days in the English channel.

interpose

Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set,n or simple thresholding techniques to identify these anomalies. We describe the application of probabilistic modeling and unsupervised learning techniques to this data set and illustrate how these approaches can successfully detect underlying systematic patterns even in the presence of substantial noise and missing data.

欲望小妹

耕種

Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned,osis in the face of non-reproducible behavior, high interactive complexity, and resource constraints. Several examples are given to finding bugs in real sensor network code using the tools developed, demonstrating the efficacy of the approach.

解決

An Adaptive Sensor Mining Framework for Pervasive Computing Applications,nt in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.

野蠻

柏樹

STELL

Pari Delir Haghighi,Brett Gillick,Shonali Krishnaswamy,Mohamed Medhat Gaber,Arkady Zaslavsky