Collaborative synthesis of visual analytic results

Visual analytic tools allow analysts to generate large collections of useful analytical results. We anticipate that analysts in most real world situations will draw from these collections when working together to solve complicated problems. This indicates a need to understand how users synthesize multiple collections of results. This paper reports the results of collaborative synthesis experiments conducted with expert geographers and disease biologists. Ten participants were worked in pairs to complete a simulated real-world synthesis task using artifacts printed on cards on a large, paper-covered workspace. Experiment results indicate that groups use a number of different approaches to collaborative synthesis, and that they employ a variety of organizational metaphors to structure their information. It is further evident that establishing common ground and role assignment are critical aspects of collaborative synthesis. We conclude with a set of general design guidelines for collaborative synthesis support tools

Leveraging Tiled Display for Big Data Visualization Using D3.js

Data visualization has proven effective at detecting patterns and drawing inferences from raw data by transforming it into visual representations. As data grows large, visualizing it faces two major challenges: 1) limited resolution i.e. a screen is limited to a few million pixels but the data can have a billion data points, and 2) computational load i.e. processing of this data becomes computationally challenging for a single node system. This work addresses both of these issues for efficient big data visualization. In the developed system, a High Pixel Density and Large Format display was used enabling the display of fine details on the screen when visualizing data. Apache Spark and Hadoop used in the system allow the computation to be done on a cluster. The system is demonstrated using a global wind flow simulation. The Global Surface Summary of the Day dataset is processed and visualized using web browsers with Data-Driven Documents (D3).js code. We conducted both a performance evaluation and a user study to measure the performance and effectiveness of the system. It was seen that the system was most efficient when visualizing data using streamed bitmap images rather than streamed raw data. The system only rendered images at 6-10 Frames Per Second (FPS) and did not meet our target of rendering images at 30 FPS. The results of the user study concluded that the system is effective and easy to use for data visualization. The outcome of our experiment suggests that the current state of Google Chrome may not be as powerful as required to perform heavy 2D data visualization on the web and still needs more development for visualizing data of large magnitude

Visualização de trajectórias humanas em dispositivos móveis

Tese de mestrado, Engenharia Informática (Sistemas de Informação), Universidade de Lisboa, Faculdade de Ciências, 2016Com a crescente popularidade de dispositivos móveis (como smartphones e tablets) e de aplicações que capturam e armazenam dados geográficos, cada vez mais pessoas gravam as suas deslocações sob a forma de dados de trajectórias. Este padrão emergente é exemplificado com o aumento do uso de aplicações, como o Endomondo ou Runtastic, que, para além de gravarem a evolução da trajectória seguida pelo utilizador, permitem a visualização desses mesmos dados, tipicamente sobre a forma de mapas estáticos 2D, complementados com vários diagramas de modo a extrair conhecimento dos dados. Os mapas animados têm emergido como uma potencial técnica para a visualização de informação de forma dinâmica sendo, geralmente, considerados como intuitivos para a detecção de relações entre a informação espacial e temporal. Apesar dos vários estudos na área da visualização de dados espácio-temporais, a aplicação deste tipo de técnicas em dispositivos móveis para representação de dados de trajectos pessoais ainda se encontra pouco explorada. Este projecto tem como objectivo estudar este problema e explorar/avaliar a adequabilidade na utilização de mapas animados para a representação de trajectos de atividades físicas em dispositivos móveis. Para isso, foi criado o protótipo PATH, uma aplicação Android para a visualização de trajectos pessoais utilizando mapas animados, e, consequentemente, um teste de usabilidade de modo a comparar diferentes tipos de representações, estáticas e animadas, de trajectórias humanas. Globalmente, os resultados sugerem que apesar de os mapas animados não beneficiarem significativamente a compreensão dos dados por parte dos utilizadores, este tipo de visualização é geralmente preferido e menos exigente, em termos de interactividade, comparativamente como mapas estáticos. Por outro lado, é important também ter em consideração o tipo/foco da animação utilizada, pois poderá afectar a usabilidade da aplicação.With the growing popularity of mobile devices (such as smartphones and tablets) and applications that capture and store geographic data, more people record their movements as trajectory data. This emerging pattern is exemplified with the increase of use of applications such as, Endomondo or Runtastic, which, in addition to recording these personal trajectories, also support their visualization and analysis, typically as static maps 2D, complemented with some diagrams. Animated maps have emerged as a potential technique for dynamic visualization of information, being usually regarded as intuitive for the analysis of spatial and temporal information. Despite several studies in spatio-temporal data visualization area, the use of this kind of technique in mobile devices, for the representation of personal trajectories, remains unexplored. This project aims to study this problem and explore / evaluate the adequacy of animated maps for the representation of trajectories related with physical activity, in a mobile device context. For that, the prototype PATH was developed, an Android application for the visualization of personal trajectories using animated maps. A usability study was also conducted, comparing different types of animated representations. Overall, the results suggest that although animated maps don’t show a significantly benefit for understanding data, this kind of technique is generally preferred when compared with static maps. On the other hand, it is important to consider that the focus of the used animation, may affect the usability of the application

PerCon: A Personal Digital Library for Heterogeneous Data Management and Analysis

Systems are needed to support access to and analysis of larger and more heterogeneous scientific datasets. Users need support in the location, organization, analysis, and interpretation of data to support their current activities with appropriate services and tools. We developed PerCon, a data management and analysis environment, to support such use. PerCon processes and integrates data gathered via queries to existing data providers to create a personal or a small group digital library of data. Users may then search, browse, visualize, annotate, and organize the data as they proceed with analysis and interpretation. Analysis and interpretation in PerCon takes place in a visual workspace in which multiple data visualizations and annotations are placed into spatial arrangements based on the current task. The system watches for patterns in the user’s data selection, exploration, and organization, then through mixed-initiative interaction assists users by suggesting potentially relevant data from unexplored data sources. In order to identify relevant data, PerCon builds up various precomputed feature tables of data objects including their metadata (e.g. similarities, distances) and a user interest model to infer the user interest or specific information need. In particular, probabilistic networks in PerCon model user interactions (i.e. event features) and predict the data type of greatest interest through network training. In turn, the most relevant data objects of interest in the inferred data type are identified through a weighted feature computation then recommended to the user. PerCon’s data location and analysis capabilities were evaluated in a controlled study with 24 users. The study participants were asked to locate and analyze heterogeneous weather and river data with and without the visual workspace and mixed-initiative interaction, respectively. Results indicate that the visual workspace facilitated information representation and aided in the identification of relationships between datasets. The system’s suggestions encouraged data exploration, leading participants to identify more evidences of correlation among data streams and more potential interactions among weather and river data