Interactive Feature Selection and Visualization for Large Observational Data

Data can create enormous values in both scientific and industrial fields, especially for access to new knowledge and inspiration of innovation. As the massive increases in computing power, data storage capacity, as well as capability of data generation and collection, the scientific research communities are confronting with a transformation of exploiting the advanced uses of the large-scale, complex, and high-resolution data sets in situation awareness and decision-making projects. To comprehensively analyze the big data problems requires the analyses aiming at various aspects which involves of effective selections of static and time-varying feature patterns that fulfills the interests of domain users. To fully utilize the benefits of the ever-growing size of data and computing power in real applications, we proposed a general feature analysis pipeline and an integrated system that is general, scalable, and reliable for interactive feature selection and visualization of large observational data for situation awareness. The great challenge tackled in this dissertation was about how to effectively identify and select meaningful features in a complex feature space. Our research efforts mainly included three aspects: 1. Enable domain users to better define their interests of analysis; 2. Accelerate the process of feature selection; 3. Comprehensively present the intermediate and final analysis results in a visualized way. For static feature selection, we developed a series of quantitative metrics that related the user interest with the spatio-temporal characteristics of features. For timevarying feature selection, we proposed the concept of generalized feature set and used a generalized time-varying feature to describe the selection interest. Additionally, we provided a scalable system framework that manages both data processing and interactive visualization, and effectively exploits the computation and analysis resources. The methods and the system design together actualized interactive feature selections from two representative large observational data sets with large spatial and temporal resolutions respectively. The final results supported the endeavors in applications of big data analysis regarding combining the statistical methods with high performance computing techniques to visualize real events interactively

FSEA 2014 – Proceedings of the AVI 2014 Workshop on Fostering Smart Energy Applications through Advanced Visual Interfaces

It is with great pleasure that we welcome you to FSEA 2014, the AVI 2014 workshop on Fostering Smart Energy Applications through Advanced Visual Interfaces. This workshop focuses on advanced interaction, interface, and visualization techniques for energy-related applications, tools, and services. It brings together researchers and practitioners from a diverse range of background, including interaction design, human-computer interaction, visualization, computer games, and other fields concerned with the development of advanced visual interfaces for smart energy applications. FSEA 2014 is the result of the efforts of many people involved in its organization, including our programme committee, and others who have assisted us in putting this workshop together

ROLE OF GIS IN RESIDENTIAL MICROGRID

Master'sMASTER OF ENGINEERIN

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

Conem: um modelo para representação e análise de informação de redes espaciais em data warehouses

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-graduação em Ciência da Computação, Florianópolis, 2011Um Data Warehouse Espaço-Temporal (DWET) manipula concomitantemente dados convencionais, espaciais e temporais. Uma necessidade ainda não atendida pela tecnologia de DWET é o suporte à análise de informação de redes complexas de elementos espaciais. Neste sentido, este trabalho propõe um modelo para a análise de redes complexas em DWET. Inspirado em ideias da Geografia, este modelo tem por objetivo representar a estrutura da rede e os estados dos elementos que a compõem, para suportar a análise da evolução do estado de diferentes porções da rede ao longo do tempo. O modelo proposto utiliza ontologias para descrever hierarquias de tipos de elementos da rede, baseadas em conceitualizações específicas do domínio de aplicação, além de ontologias sobre partições do espaço e do tempo. Dimensões de datamarts podem ser geradas a partir de visões dessas ontologias, para contemplar necessidades de análise específicas. O modelo proposto estende um modelo dimensional espaço-temporal para suportar OLAP espacial (SOLAP) com os elementos da rede, usando dimensões de análise definidas de acordo com hierarquias contidas nas ontologias. Ele também define um operador denominado Trace para permitir a análise da evolução do estado dos componentes de porções da rede, selecionadas de acordo com as dimensões de análise definidas para o datamart. O modelo proposto foi implementado em um protótipo. A interface gráfica, baseada em tabelas e mapas, está integrada ao módulo SOLAP. Ao navegar pelos mapas e tabelas apresentando resultados de operações SOLAP, outras operações SOLAP podem ser invocadas e os resultados apresentados em novos gráficos e tabelas. Um slider permite a análise da evolução temporal do estado de porções da rede. Por fim, a modelo é avaliado em um estudo de caso do setor elétrico, o qual possibilita a investigação de padrões e tendências espaço-temporais em diferentes porções de uma rede de distribuição de energia elétrica