Knowledge Discovery from Sensor Data

Knowledge Discovery from Sensor Data

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Summary

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.

Table of Contents

A Probabilistic Framework for Mining Distributed Sensory Data Under Data Sharing Constraints, J. Ghosh

A General Framework for Mining Massive Data Streams, P. Domingos and G. Hulten

A Sensor Network Data Model for the Discovery of Spatio-Temporal Patterns, B. George, J.M. Kang, and S. Shekhar

Requirements for Clustering Streaming Sensors, P.P. Rodrigues, J. Gama, and L. Lopes

Principal Component Aggregation for Energy-Efficient Information Extraction in Wireless Sensor Networks, Y.-A. Le Borgne, J.-M Dricot, and G. Bontempi

Anomaly Detection in Transportation Corridors Using Manifold Embedding, A. Agovic, A. Banerjee, A.R. Ganguly, and V. Protopopescu

Fusion of Vision Inertial Data for Automatic Georeferencing, D.I.B. Randeniya, M. Gunaratne, and S. Sarkar

Electricity Load Forecast Using Data Streams Techniques, J. Gama and P.P. Rodrigues

Missing Event Prediction in Sensor Data Streams Using Kalman Filters, N.N. Vijayakumar and B. Plale

Mining Temporal Relations in Smart Environment Data Using TempAl, V.R. Jakkula and D.J. Cook

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