
Knowledge Discovery from Sensor Data
1st Edition
Published on 19. September 2019
Book
Paperback/Softback
215 pages
978-0-367-38623-8 (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 analysis of sensor or geographically distributed data. It discusses the challenges and requirements for sensor data based knowledge discovery solutions in high-priority application illustrated with case studies. It explores the fusion between heterogeneous data streams from multiple sensor types and applications in science, engineering, and security.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional Practice & Development
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 13 mm
Weight
368 gr
ISBN-13
978-0-367-38623-8 (9780367386238)
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
12/2008
1st Edition
CRC Press
€215.41
Article not available at the moment

Auroop R. Ganguly | Joao Gama | Olufemi A. Omitaomu
Knowledge Discovery from Sensor Data
E-Book
12/2008
CRC Press
€92.49
Available for download

Auroop R. Ganguly | Joao Gama | Olufemi A. Omitaomu
Knowledge Discovery from Sensor Data
E-Book
12/2008
1st Edition
CRC Press
€92.49
Available for download
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.