Design of interactive visualization of models and students data

This document reports the design of the interactive visualizations of open student models that will be performed in GRAPPLE. The visualizations will be based on data stored in the domain model and student model, and aim at supporting learners to be more engaged in the learning process, and instructors in assisting the learners

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move

Explorative Graph Visualization

Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization

Adaptive user interfaces for mobile map-based visualisation

Mobile devices today frequently serve as platforms for the visualisation of map-based data. Despite the obvious advantages, mobile map-based visualisation (MMV) systems are often difficult to design and use. Limited screen space, resource constraints and awkward interaction mechanisms are among the many problems with which designers and users have to contend. Adaptive user interfaces (AUIs), which adapt to the individual user, represent a possible means of addressing the problems of MMV. Adaptive MMV systems are, however, generally designed in an ad-hoc fashion, making the benefits achieved difficult to replicate. In addition, existing models for adaptive MMV systems are either conceptual in nature or only address a subset of the possible input variables and adaptation effects. The primary objective of this research was to develop and evaluate an adaptive MMV system using a model-based approach. The Proteus Model was proposed to support the design of MMV systems which adapt in terms of information, visualisation and user interface in response to the user‟s behaviour, tasks and context. The Proteus Model describes the architectural, interface, data and algorithm design of an adaptive MMV system. A prototype adaptive MMV system, called MediaMaps, was designed and implemented based on the Proteus Model. MediaMaps allows users to capture, location-tag, organise and visualise multimedia on their mobile phones. Information adaptation is performed through the use of an algorithm to assist users in sorting media items into collections based on time and location. Visualisation adaptation is performed by adapting various parameters of the map-based visualisations according to user preferences. Interface adaptation is performed through the use of adaptive lists. An international field study of MediaMaps was conducted in which participants were required to use MediaMaps on their personal mobile phones for a period of three weeks. The results of the field study showed that high levels of accuracy were achieved by both the information and interface adaptations. High levels of user satisfaction were reported, with participants rating all three forms of adaptation as highly useful. The successful implementation of MediaMaps provides practical evidence that the model-based design of adaptive MMV systems is feasible. The positive results of the field study clearly show that the adaptations implemented were highly accurate and that participants found these adaptations to be useful, usable and easy to understand. This research thus provides empirical evidence that the use of AUIs can provide significant benefits for the visualisation of map-based information on mobile devices

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment