Table of contents
About this book
This book presents a framework for converting multitudes of data streams available today including weather patterns, stock prices, social media, traffic information, and disease incidents into actionable insights based on situation recognition. It computationally defines the notion of situations as an abstraction of millions of data points into actionable insights, describes a computational framework to model and evaluate such situations and presents an open-source web-based system called EventShop to implement them without necessitating programming expertise.
The book is useful for both practitioners and researchers working in the field of situation-aware computing. It acts as a primer for data-enthusiasts and information professionals interested in harnessing the value of heterogeneous big data for building diverse situation-based applications. It also can be used as a reference text by researchers working in areas as varied as database design, multimodel concept recognition, and middle-ware and ubiquitous computing to design and develop frameworks that allow users to create their own situation recognition frameworks.
Big data EventShop Multimodal data fusion Situation operators Situation recognition Situation detection Data fusion Spatio-temporal data mining Situation modeling Data analytics Multimedia Smart cities
- DOI https://doi.org/10.1007/978-3-319-30537-0
- Copyright Information The Author(s) 2016
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Print ISBN 978-3-319-30535-6
- Online ISBN 978-3-319-30537-0
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