Knowledge Discovery from Sensor Data

ISBN-13: 9780367386238 / Angielski / Miękka / 2019 / 215 str.

Auroop R. Ganguly; Joao Gama; Olufemi a. Omitaomu

Opis

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.

Spis treści

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.

O autorze

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