Knowledge Discovery from Sensor Data

Auroop R. Ganguly, Joao Gama, Olufemi A. Omitaomu, Mohamed Gaber, Ranga Raju Vatsavai (Herausgeber)

Buch | Softcover

215 Seiten

2019
CRC Press (Verlag)
978-0-367-38623-8 (ISBN)

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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.

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

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.

Erscheinungsdatum	24.09.2019
Verlagsort	London
Sprache	englisch
Maße	156 x 234 mm
Gewicht	440 g
Themenwelt	Kunst / Musik / Theater ► Design / Innenarchitektur / Mode
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
ISBN-10	0-367-38623-2 / 0367386232
ISBN-13	978-0-367-38623-8 / 9780367386238
Zustand	Neuware