The rapid proliferation of interconnected devices within the Internet of Things (IoT) ecosystem has led to an exponential surge in data generation and consumption. This expanding phenomenon has highlighted the importance of efficient management and effective representation of big data. A prominent contributor to this surge of big data is sensor networks which generate geotagged data covering a wide range of environmental and electromechanical conditions. This thesis aims to handle this expanding data landscape by developing an application that incorporates powerful cartographic and graphical features. It was deemed necessary for the existence of two distinct visualization technologies that would be harmoniously integrated under one framework. In view of this need, research was conducted to select the optimized technologies. Subsequently, an in-depth architectural analysis of the application will be carried out with the purpose of capturing the system's fundamental architectural qualities. This analysis will demonstrate how these architectural components precisely interconnect and augment the application's functional requirements. Concluding this thesis, the application's implementation will be meticulously carried out, offering a detailed roadmap for seamless operations throughout its interface. To elaborate further, distinct iterations of the user's navigational experiences will be methodically analyzed, facilitating the exploration of the system's features intuitively.
Keywords
Internet of ThingsBig Data ManagementCartographic Visualization
Institute(s)
HELLENIC MEDITERRANEAN UNIVERSITY
Year
2023
Abstract
Author(s)
Giannis Gerolymos