" More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it. As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user's interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques. A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis. A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time. "

This thesis examines the usage of and attitudes toward data visualization and analytic tools in three genres of online games. Using an online survey, this research analyzes responses from participants regarding their play habits and attitudes online. Several scales are generated identifying different player demographics such as emotional attitudes, competitive attitudes, technological attitudes, spectator involvement, and overall attitudes toward information customization. In addition, several genre specific scales are created for massive multiplayer online games (MMO), real time strategy (RTS) and first person shooting (FPS) games. This research concludes that competitive attitudes are moderately correlated with information customization and implementation of data visualization tools. Additionally, the relationship between the usage of data visualization tools are strongest with the MMO genre compared to the RTS or FPS genres. In addition, my research shows a strong preference between the responses for the usage of data visualization tools amongst those who report higher levels of spectator involvement with online games. Finally, my research concludes that there is a strong relationship between the amount of time players spend playing online games and the attitudes toward and usage of data visualization tools. / text

This dissertation focuses on the development of visualization methods that enable us to examine longitudinal data in a unique way. We take inspiration from statistical and fluid mechanics to represent our data as a "flow" through time. Our visualizations represent vector fields (or flow plots), streamlines, and trajectories, and they are constructed in a similar manner to how one might analyze the aggregate motion of particles in a fluid. However, the subject of our research extends beyond ordinary fluid mechanics. We will use our visualizations to examine statewide standardized test scores in Texas from 2003 to 2011. The nature of the data makes it a perfect match for our methodology, since students' test scores tend to change over time in a semi-deterministic but nonlinear manner. Furthermore, our methods represent a departure from the standard ways of analyzing educational data. By visualizing the changes in students' test scores over a nine-year period, we discovered that our flow plots were changing with the eventual graduating class of 2012. The change in our visualizations was caused by an educational policy known as the Student Success Initiative, or SSI. The policy forced students to pass their standardized tests in 5th and 8th grade, or risk being held back a grade. To help with this process, students who initially failed were given extra instruction and additional opportunities to take the test. SSI was implemented in such a way that it would affect the class of 2012 and beyond, although we did not know of the program's existence until our plots had been developed. SSI had a successful impact on the educational career of Texas students; a far greater percentage of students were able to pass the 5th and 8th grade standardized tests after SSI was implemented. The striking feature of SSI, however, is that it also significantly improved test scores in 6th, 7th, 9th, and 10th grade. Despite its success at improving test scores over many years and grades, the program was eventually defunded. This was partially due to an inability to construct a lengthy longitudinal analysis of the program's influence. Our methodology would have conclusively shown the effectiveness of the SSI policy. Despite the defunding of the SSI, I am confident our methodology can be extended to illustrate changes in other data systems. These systems may or may not be related to education; our code and techniques are designed to be as universal as possible. We will explore several extensions to other data sets at the end of this dissertation. / text

For my MFA, I have decided to explore data visualization not as an automated technology but as a craft—a systematic and precise practice done entirely by hand. Though the craft-based approach is not appropriate for all types of data creation and visualization, as an investigatory tool it grants a level of access and intimacy lacking in computerized analyses. I discuss the limitations and benefits of this type of approach, as well as provide an overview of key influences and precedents. I have also included select projects developed over the course of my studies that highlight my use of data visualization for a range of subjects and intents, including reading piano sheet music more easily and investigating a photographer’s compositional process. The report concludes by projecting how this craft-based approach for data visualization may be integrated with an automated method. / text

This thesis presents a system to display Structural Health Monitoring (SHM) data interactively at multiple scales that range from milliseconds to years. Typically, visualizing large SHM datasets produce static plots that take significant time to render. Our system improves upon standard tools by providing an interactive interface and a speed-optimized binning algorithm. Using the interface, a user is able to view data collected from a bridge's sensors at multiple scales in a web browser. This allows a user to visually inspect the entire range of their data to see both short and long-term trends. To render the data, the system uses a binning algorithm to calculate a five-number summary of a range of data. Those bins are combined to generate increasingly high levels of bins, which are then rendered as a binned line chart. The chart is rendered using a standard web browser on both desktop and mobile devices.

With the complexity increase of enterprise solutions, the need to monitor and maintain them increases with it. SAP Concur offers various services and applications across different environments and data centers. For all these applications and the services underneath, there are different Application Performance Management (APM) tools in place for monitoring them. However, from an incident management point of view, in case of a problem it is time consuming and non-efficient to go through different tools in order to identify the issue. This thesis proposes a solution for a custom and centralized APM which gathers metrics and raw data from multiple sources and visualizes them in real-time in a unified health dashboard called Pulse. In order to fit this solution to the needs of service managers and product owners, Pulse will go through different phases of usability tests and after each phase, new requirements will be implemented and tested again until there is a final design that fits the needs of target users.

The eerie feeling attributed to human-looking robots and animated characters may be a key factor in our perceptual and cognitive discrimination between the human and the merely humanlike. This study applies factor analysis, correlation, the generalized linear model (GLM), multidimensional scaling (MDS), and kernel isometric mapping (ISOMAP) to analyze ratings of 27 emotions of 16 moving figures whose appearance varies along a human likeness continuum. The results indicate (1) Attributions of eerie and creepy better capture human visceral reaction to an uncanny robot than strange. (2) Eeriness and creepiness are mainly associated with fear but also shocked, disgusted, and nervous. Strange and humanlike are less strongly associated with emotion. (3) Thus, strange and humanlike may be more cognitive, while eerie and creepy are more perceptual and emotional. (4) Human and facial features increase ratings of human likeness. (5) Women are slightly more sensitive to eerie and creepy than men; and older people may be more willing to attribute human likeness to a robot despite its eeriness.

As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development of tools and knowledge to assist with its analysis, modeling, and forecasting. To do this, three research objectives were fulfilled. First, the Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to improve upon existing real-time hydrologic and meteorological data retrieval/visualization platforms through the integration of analysis tools to study the hydrologic processes influencing urban flooding. Next, the ability to simulate the hydrologic response of urban watersheds with large storm sewer networks was compared between the fully distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model and the semi-distributed Storm Water Management Model (SWMM). Finally, the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application was created to help users evaluate the probability of urban flash flooding and to identify specific infrastructure components at risk through the integration of high-resolution quantitative precipitation forecasting, ensemble forecasting, and hydrologic and hydraulic modeling. The outcomes of this dissertation provide municipalities with tools and knowledge to assist them throughout the process of developing solutions to their site-specific urban flooding issues. Specifically, tools are provided to rapidly analyze and respond to rainfall and streamflow/depth information during intense rain events and to perform retrospective analysis of long-term hydrological processes. Evaluations are included to help guide the selection of hydrologic and hydraulic models for modeling urban flooding, and a new proactive paradigm of probabilistic flash flood guidance for urban areas is introduced. Finally, several potential directions for future work are recommended. / Doctor of Philosophy / As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development of tools and knowledge to assist with its analysis, modeling, and forecasting. To do this, three research objectives were fulfilled. First, the Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to improve upon existing real-time hydrologic and meteorological data retrieval/visualization platforms through the integration of analysis tools to study the hydrologic processes influencing urban flooding. Next, the ability to simulate the hydrologic response of urban watersheds with large storm sewer networks was compared between the fully distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model and the semi-distributed Storm Water Management Model (SWMM). Finally, the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application was created to help users evaluate the probability of urban flash flooding and to identify specific infrastructure components at risk through the integration of high-resolution quantitative precipitation forecasting, ensemble forecasting, and hydrologic and hydraulic modeling. The outcomes of this dissertation provide municipalities with tools and knowledge to assist them throughout the process of developing solutions to their site-specific urban flooding issues. Specifically, tools are provided to rapidly analyze and respond to rainfall and streamflow/depth information during intense rain events and to perform retrospective analysis of long-term hydrological processes. Evaluations are included to help guide the selection of hydrologic and hydraulic models for modeling urban flooding, and a new proactive paradigm of probabilistic flash flood guidance for urban areas is introduced. Finally, several potential directions for future work are recommended.

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Through the use of bio-metric data and audio recordings this research explores the body as it engages the concert environment. With the help of wearable technology and digital audio recording devices, data from four concerts was documented. Additionally personal reflections were recorded at the conclusion of each concert. These documents serve as qualitative data and a point of comparison between the quantitative recordings. These records were then used in the construction of an interactive data visualization that allows further exploration of the data collected by means of a visual interface. / Master of Fine Arts