Knowledge Discovery from Sensor Data
Coordonnateurs : Ganguly Auroop R., Gama Joao, Omitaomu Olufemi A., Gaber Mohamed, Vatsavai Ranga Raju
A Probabilistic Framework for Mining Distributed Sensory Data Under Data Sharing Constraints. A General Framework for Mining Massive Data Streams. A Sensor Network Data Model for the Discovery of Spatio-Temporal Patterns. Requirements for Clustering Streaming Sensors. Principal Component Aggregation for Energy-Efficient Information Extraction in Wireless Sensor Networks. Anomaly Detection in Transportation Corridors Using Manifold Embedding. Fusion of Vision Inertial Data for Automatic Georeferencing. Electricity Load Forecast Using Data Streams Techniques. Missing Event Prediction in Sensor Data Streams Using Kalman Filters. Mining Temporal Relations in Smart Environment Data Using TempAl. Index.
Date de parution : 09-2019
15.6x23.4 cm
Date de parution : 12-2008
Ouvrage de 214 p.
15.6x23.4 cm
Thèmes de Knowledge Discovery from Sensor Data :
Mots-clés :
Sensor Networks; Anomaly Detection; Kalman Filter; Sensor Node; Im En; Aggregation Service; METAR Data; Smart Home; Routing Tree; WSN; Tra Ld; ISOMAP; Massive Data Streams; Anomaly Detection Methods; Dynamic Linear Model; Smart Environment; Network Load; Case Study; Data Set; Spatio Temporal Data Mining; Sensor Graph; INESC Porto; Synthetic Datasets; Its; Gps Outage