Knowledge Discovery from Sensor Data
1st Edition
Published on 10. December 2008
215 pages
978-1-4200-8233-3 (ISBN)
Description
As sensors become ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. This book presents innovative solutions in offline data mining and real-time
More details
Edition
1. Auflage
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Industrial, manufacturing, computer science, and environmental engineers, GIS professionals.
Illustrations
119 s/w Abbildungen
119 b/w images
ISBN-13
978-1-4200-8233-3 (9781420082333)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Auroop R. Ganguly | Joao Gama | Olufemi A. Omitaomu
Knowledge Discovery from Sensor Data
Book
09/2019
1st Edition
CRC Press
€100.70
Shipment within 15-20 days
Auroop R. Ganguly | Joao Gama | Olufemi A. Omitaomu
Knowledge Discovery from Sensor Data
Book
12/2008
1st Edition
CRC Press
€215.41
Article not available at the moment
Persons
Auroop R. Ganguly, Joao Gama, Olufemi A. Omitaomu, Mohamed Medhat Gaber, Ranga Raju Vatsavai
Content
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.
