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Knowledge Discovery from Sensor Data

Editat de Auroop R. Ganguly, Joao Gama, Olufemi A. Omitaomu, Mohamed Gaber, Ranga Raju Vatsavai
en Limba Engleză Hardback – 10 dec 2008
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.
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Specificații

ISBN-13: 9781420082326
ISBN-10: 1420082329
Pagini: 215
Ilustrații: 119 b/w images
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.54 kg
Ediția:New.
Editura: CRC Press
Colecția CRC Press

Public țintă

Professional Practice & Development

Cuprins

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.

Notă biografică

Auroop R. Ganguly, João Gama, Olufemi A. Omitaomu, Mohamed Medhat Gaber, Ranga Raju Vatsavai

Descriere

Addressing the issues challenging the sensor community, this book presents innovative solutions in offline data mining and real-time analysis of sensor or geographically distributed data. Illustrated with case studies, it discusses the challenges and requirements for sensor data-based knowledge discovery solutions in high-priority application. The book then explores the fusion between heterogeneous data streams from multiple sensor types and applications in science, engineering, and security. Bringing together researchers from academia, government, and the private sector, this book delineates the application of knowledge modeling in data intensive operations.