Visual insight of spatiotemporal IoT-generated contents

Abstract

The rapid evolution of the Internet of Things (IoT) and Big Data technology has been generating a large amount and variety of sensing contents, including numeric measured values (e.g., timestamps, geolocations, or sensor logs) and multimedia (e.g., images, audios, and videos). In analyzing and understanding heterogeneous types of IoT-generated contents better, data visualization is an essential component of exploratory data analyses to facilitate information perception and knowledge extraction. This study introduces a holistic approach of storing, processing, and visualizing IoT-generated contents to support context-aware spatiotemporal insight by combining deep learning techniques with a geographical map interface. Visualization is provided under an interactive web-based user interface to help the an efficient visual exploration considering both time and geolocation by easy spatiotemporal query user interface1.