University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

While linguistic skill is a hallmark of humanity, the increasing volume of linguistic data each of us faces is causing individual and societal problems — ‘information overload’ is a commonly discussed condition. Tasks such as finding the most appropriate information online, understanding the contents of a personal email repository, and translating documents from another language are now commonplace. These tasks need not cause stress and feelings of overload: the human intellectual capacity is not the problem. Rather, the computational interfaces to linguistic data are problematic — there exists a Linguistic Visualization Divide in the current state-of-the-art. Through five design studies, this dissertation combines sophisticated natural language processing algorithms with information visualization techniques grounded in evidence of human visuospatial capabilities. The first design study, Uncertainty Lattices, augments real-time computermediated communication, such as cross-language instant messaging chat and automatic speech recognition. By providing explicit indications of algorithmic confidence, the visualization enables informed decisions about the quality of computational outputs. Two design studies explore the space of content analysis. DocuBurst is an interactive visualization of document content, which spatially organizes words using an expert-created ontology. Broadening from single documents to document collections, Parallel Tag Clouds combine keyword extraction and coordinated visualizations to provide comparative overviews across subsets of a faceted text corpus. Finally, two studies address visualization for natural language processing research. The Bubble Sets visualization draws secondary set relations around arbitrary collections of items, such as a linguistic parse tree. From this design study we propose a theory of spatial rights to consider when assigning visual encodings to data. Expanding considerations of spatial rights, we present a formalism to organize the variety of approaches to coordinated and linked visualization, and introduce VisLink, a new method to relate and explore multiple 2d visualizations in 3d space. Intervisualization connections allow for cross-visualization queries and support high level comparison between visualizations. From the design studies we distill challenges common to visualizing language data, including maintaining legibility, supporting detailed reading, addressing data scale challenges, and managing problems arising from semantic ambiguity.

In a time of 'Big Data,' 'Personal Informatics' and 'Infographics' the definitions of data visualization and data analytics are splintering rapidly. When one compares how Fortune 500 companies are using analytics to optimize their supply chains and lone individuals are visualizing their Twitter messages, we can see how multipurpose these areas are becoming. Visualization and analytics are frequently exhibited as tools for increasing efficiency and informing future decisions. At the same time, they are used to produce artworks that alter our perspectives of how data is represented and analyzed. During this time of turbulent reflection within the fields of data visualization and analytics, digital games have been going through a similar period of data metamorphosis as players are increasingly being connected and tracked through various platform systems and social networks. The amount of game-related data collected and shared today greatly exceeds that of previous gaming eras and, by utilizing the domains of data visualization and analytics, this increased access to data is poised to reshape, and continue to reshape, how players experience games. This dissertation examines how visualization, analytics and games intersect into a domain with a fluctuating identity but has the overall goal to analyze game-related data. At this intersection exists play analytics, a blend of digital systems and data analysis methods connecting players, games and their data. Play analytic systems surround the experience of playing a game, visualizing data collected from players and act as external online hubs where players congregate. As part of this dissertation's examination of play analytics, over eighty systems are analyzed and discussed. Additionally, a user study was conducted to test the effects play analytic systems have on a player's gameplay behavior. Both studies are used to highlight how play analytic systems function and are experienced by players. With millions of players already using play analytics systems, this dissertation provides a chronicle of the current state of play analytics, how the design of play analytics systems may shift in the future and what it means to play with data.

Visualization of Sensory Perception Descriptors is a topic in the field of Information Visualization. It is concentrated on the research and development of methods for analyses of data related with human modalities description. One possibility for investigating sensory perception descriptors is analyzing a great number of wine tasting notes. This thesis is concerned with the visualization of wine tasting notes in order to aid linguistic analyses. It strives to find proper visualizations that will give a better insight into the language used in wine tasting notes. Two main processes are described in the following report. First it sets out the process of researching of different methods of information visualization that led to the final approach for representing the data. A number of concepts for text analyses are discussed and the most useful of them are developed further. Several approaches for text visualization and statistical information are combined to build a system for tasting notes analyses. The second part of the report describes the process of developing a prototype that implements the represented approaches and gives an opportunity for real testing and conclusions.

Today’s computing vision makes users to access services, applications via lightweight portable devices instead of powerful personal computers (PC). Since today’s applications and services need strong computing power and data storage, raising question will be “Who will provide these 2 attributes if users do not?” Cloud computing trend moves computing power and data storage from users’ side to application infrastructure side. The services that traditionally stored in users’ own computers will move into cloud computing platform and delivered by the Internet to its users. This new platform comes with its own benefits and design characteristics. Since all information data will move into another platform than individual computers, information visualization will be an opportunity field to analyze and maintain the cloud system structure as well as delivering abstract data into meaningful way to end users.

Collaborative tagging is a simple and effective method for organizing and sharing web resources using human created metadata. It has arisen out of the need for an efficient method of personal organization, as the number of digital resources in everyday lives increases. While tagging has become a proven organization scheme through its popularity and widespread use on the Web, little is known about its implications and how it may effectively be applied in different situations. This is due to the fact that tagging has evolved through several iterations of use on social software websites, rather than through a scientific or an engineering design process. The research presented in this thesis, through investigations in the domain of e-learning, seeks to understand more about the scientific nature of collaborative tagging through a number of human subject studies. While broad in scope, touching on issues in human computer interaction, knowledge representation, Web system architecture, e-learning, metadata, and information visualization, this thesis focuses on how collaborative tagging can supplement the growing metadata requirements of e-learning. I conclude by looking at how the findings may be used in future research, through using information based in the emergent social networks of social software, to automatically adapt to the needs of individual users.

Parametric visual effects such as texture generation and shape grammars can be controlled to produce visually perceptible variation. This variation can be rendered on avatars in groupware systems in real time to represent user information in online environments. This type of extra information has been shown to enrich recognition and characterization, but has previously been limited to iconic representations. Modern, highly graphical virtual worlds require more naturalistic and stylistically consistent techniques to represent information.<p> A number of different parametric texture generation techniques are considered and a set of texture characteristics are developed. The variations of these texture characteristics are examined in a study to determine how well users can recognize the visual changes in each. Another study is done to determine how much screen space is required for users to recognize these visual changes in a subset of these texture characteristics.<p> Additionally, an example shape generation system is developed as an example of how shape grammars and L-systems can be used to represent information using a space ship metaphor.<p> These different parametric visual effects are implemented in an example prototype system using space ships. This prototype is a complete functioning groupware application developed in XNA that utilizes many parametric texture and shape effects.

Visualization could be defined as the graphical presentation of information, in which the main aim is to improve the user&rsquo / s perception. In all phases of the disaster management, decision makers come across huge data sets with spatio-temporal content. It is hard to deal with these sets in order to find answers to the main question of &ldquo / How can we decrease the losses due to disasters?&rdquo / , which is at the core of the disaster management concept. To furnish this aim, disaster risk information has to be transparent and clearly stated to the public, decision makers and disaster managers. This might be more sophisticated than the calculation of the risk. Taking precautions before a disaster to reduce the causalities and lossess engendered by natural disasters is relatively cheaper, and more importantly, better than cure. To achieve enhanced preparations for all kinds of disasters, visualization is quite an important tool for decision support and risk communication. The basic aim of this research is to propose a conceptual framework, with the consideration of all stakeholders related to the disaster management issue to have a better risk communication, and to guide the design, implementation and integration of the 3D urban modeling tools into disaster risk visualization. Moreover, an empirical methodology is also developed for the generation of visualization solutions through the design, and employment of the tool for disaster management framework. The proposed framework has three main phases .These are the definition of visualization components, object representation, and needs assessment. A new LoD hierarchy with indoor is proposed to visualize all the possible 3D urban disaster situations in the first phase. Then, a decision rule with eight attributes is proposed in the second phase to establish a link between the hazard type and the LoD needed in a 3D urban model for visualization. This decision rule is applied in a proposed three-level hierarchycal structure. The assessed objects of these three levels are urban, sub-urban zone and building. Moreover, a method to define the needed sub-urban zone is proposed. Finally, different 3D urban modelling methods are analyzed to define the data and process needs of possible 3D urban disaster visualization situations. Two natural hazard cases are studied within the scope of this dissertation to assess the operability of the proposed framework. These implementations involve one earthquake and one tsunami case. Special attention is paid to finding one specific sample for two modelling viewpoints, namely static and dynamic. The first applications of the proposed framework with all the related features prove quite promising.

Email has developed into one of the most extensively used computer applications. Email interfaces, on the other hand, have gone through very few transformations since their inception. As the growing volumes of email data accumulate in users' email boxes, these interfaces fail to provide effective message handling and browsing support. Saved email messages provide not only a vast and pulsating record of one's electronic past, but also a potential source of valuable insights into the structure and dynamics of one's social network. In this thesis, we introduce a visualization approach to email that draws upon email’s inherently personal character and uses intimacy as a key parameter. We have developed faMailiar, a novel email interface that visualizes email in a chronological manner through two alternative, calendar-like views that present email activity on different time granularity scales. Visual mappings of email data and support for filtering help the user see rhythms and patterns in her social interactions. Zooming, panning and implicit semantic zooming facilitate navigation across large email collections. This thesis also describes our iterative, human-centered design method. Two user studies have been performed at different stages of the process, and we explain their purpose, results and implications.

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. This dissertation is motivated by the observation that a general approach for performing multi-dimensional and multi-level network-based visual analysis on multivariate tabular data is necessary. We present a formal framework based on the relational data model that systematically specifies the construction and transformation of graphs from relational data tables. In the framework, a set of relational operators provide the basis for rich expressive power for network modeling. Powered by this relational algebraic framework, we design and implement a visual analytics system called Ploceus. Ploceus supports flexible construction and transformation of networks through a direct manipulation interface, and integrates dynamic network manipulation with visual exploration for a seamless analytic experience.