As scientific simulations get increasingly complex, so do the requirements of how to deal with the data that is produced. Few scientists and engineers today are satisfied with just looking at streams of numbers; we require graphical visualizations to better understand their meaning. The traditional method of visualization has been to save the simulation's results to a file, then load that file up in another program (eg. Microsoft Excel) for post-processing. Although post-processing data to produce visualizations may be sufficient for some simple simulations, a modern simulation designer usually wants more out of their visualization. Perhaps they want the visualization to be a 3D plot of an interplanetary trajectory, with the ability to zoom, pan, and rotate the scene interactively. Until now, doing so has required the designer to become adept at computer graphics, which is a feat that almost no scientist or engineer has the time to attempt. The research undertaken here introduces an architecture by which a simulation programmer can easily add interactive 3D visualizations to their simulations. This architecture has several benefits over existing visualization packages, the biggest one being that no knowledge of computer graphics is required to use the it in one's own simulations. Another benefit is that the resulting visualization is interactive by default, without any extra programming required on the part of the simulation designer. This thesis begins by introducing the theory behind how scientific simulations want to visualize data. Common aspects of all simulations are identified, and are used to develop a common "visualization language" that can be used by any simulation designer to specify what they want to visualize. The second part of the thesis specifies a particular implementation of this visualization language, called OpenFrames. Open- Frames is a library of functions that can be called from C, C++, or FORTRAN, and automatically implements the visualization specified by the designer.

The annual cycles of human- and lightning-caused fires create distinct patterns in time and space. Evaluating these patterns reveals intimate relationships between climate, culture, and ecoregions. I used unique graphical visualization techniques to examine a dataset of 516,691 records of human- and lightning-caused fire-start data from the western United States for the 20-year period 1992-2011. Human-caused fires were ignited throughout the year and near human populations, while lightning-caused fires were confined almost exclusively to the summer and were concentrated in less-populated areas. I utilize graphs and maps to demonstrate the benefit of a longer time frame in strengthening the findings and describing the underlying interactions among climate, society, and biogeography. / 2016-01-14

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.

The effectiveness of direct volume rendered images depends on finding transfer functions which emphasize structures in the underlying data. In order to support this process, we present a spreadsheet-like constructive visual component-based interface, which also allows novice users to efficiently find meaningful transfer functions. The interface uses a programming-by-example style approach and exploits the domain knowledge of the user without requiring visualization knowledge. Therefore, our application automatically analysis histograms with the Douglas-Peucker algorithm in order to identify potential structures in the data set. Sample visualizations of the resulting structures are presented to the user who can refine and combine them to more complex visualizations. Preliminary tests confirm that the interface is easy to use, and enables non-expert users to identify structures which they could not reveal with traditional transfer function editors. / <p>Short paper</p>

Code clones are identical or similar code fragments in a single software system or across multiple systems. Frequent copy-paste-modify activities and reuse of existing systems result in maintenance difficulties and security issues. Addressing these problems requires analysts to undertake code clone analysis, which is an intensive process to discover problematic clones in existing software. To improve the efficiency of this process, tools for code clone detection and analysis, such as Kam1n0 and CCFinder, were created. Kam1n0 is an efficient code clone search engine that facilitates assembly code analysis. However, Kam1n0 search results can contain millions of function-clone pairs, and efficiently exploring and comprehensively understanding the resulting data can be challenging. This thesis presents a design study whereby we collaborated with analyst stakeholders to identify requirements for a tool that visualizes and scales to millions of function-clone pairs. These requirements led to the design of an interactive visual tool, CloneCompass, consisting of novel TreeMap Matrix and Adjacency Matrix visualizations to aid in the exploration of assembly code clones extracted from Kam1n0. We conducted a preliminary evaluation with the analyst stakeholders, and we show how CloneCompass enables these users to visually and interactively explore assembly code clones detected by Kam1n0 with suspected vulnerabilities. To further validate our tool and extend its usability to source code clones, we carried out a Linux case study, where we explored the clones in the Linux kernel detected by CCFinder and gained a number of insights about the cloning activities that may have occurred in the development of the Linux kernel. / Graduate

With the emergence of Big Data, data high in volume, variety, and velocity, new analysis techniques need to be developed to effectively use the data that is being collected. Knowledge discovery from databases is a larger methodology encompassing a process for gathering knowledge from that data. Analytics pair the knowledge with decision making to improve overall outcomes. Organizations have conclusive evidence that analytics provide competitive advantages and improve overall performance. This paper proposes a stand-alone methodology for data exploration. Data exploration is one part of the data mining process, used in knowledge discovery from databases and analytics. The goal of the methodology is to reduce the amount of time to gain meaningful information about a previously unanalyzed data set using tabular summaries and visualizations. The reduced time will enable faster implementation of analytics in an organization. Two case studies using a prototype implementation are presented showing the benefits of the methodology.

Command line and version control skills are vital to computer science students during their education and as they enter the software industry. These skills are commonly taught to undergraduate students via traditional lecturing methods and brief hands-on activities. Many students struggle with learning the Git version control system because they are not familiar with the command line, or they do not understand how Git works internally. Recent research highlights the effectiveness of using interactive visualizations to teach computer science concepts. Thus, we developed novel command line and Git exercises with interactive visualizations. These exercises integrate with learning management systems to automate grading. We tested the effectiveness of the exercises in a CS2 course at a large research institution by conducting pre-assessments before and post-assessments after the students completed the exercises. We found that students performed significantly better on both the command line and Git post-assessments than on the pre-assessments. Furthermore, we found that students with less experience with the command line and Git achieved a significantly greater improvement from the pre-assessments to the post-assessments. Additionally, we found that students with different levels of command line and Git experience did not perform differently on the exercises. Therefore, the exercises provide a novel tool for teaching command line and Git concepts to undergraduate computer science students with any level of command line and Git experience. / Master of Science / Command line is a term used to refer to a text-based user interface that allows users to interact with their computers by inputting commands. Git is a version control system typically used to track the stages of development for a computer program. Command line and Git skills are vital to computer science students during their education and as they enter the software industry. These skills are commonly taught to undergraduate students via traditional lecturing methods and brief hands-on activities. Many students struggle with Git because they are not familiar with the command line, or they do not understand how Git works internally. Recent research highlights the effectiveness of using interactive visualizations to teach computer science concepts. Thus, we developed novel command line and Git exercises with interactive visualizations. These exercises integrate with learning management systems to automate grading. We tested the effectiveness of the exercises in a CS2 course at a large research institution by conducting pre-assessments before and post-assessments after the students completed the exercises. We found that students performed significantly better on the post-assessments than on the pre-assessments. Furthermore, we found that students with less experience with the command line and Git achieved a significantly greater improvement from the pre-assessments to the post-assessments. Therefore, the exercises provide a novel tool for teaching command line and Git concepts to undergraduate computer science students with any level of command line and Git experience.

This dissertation is the compilation of two major innovations that rely on a common technique known as multidimensional scaling (MDS). MDS is a dimension-reduction method that takes high-dimensional data and creates low-dimensional versions. Project 1: Visualizations are useful when learning from high-dimensional data. However, visualizations, just as any data summary, can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. We incorporate uncertainty into visualizations created by a weighted version of MDS called WMDS. Uncertainty exists in these visualizations on the variable weights, the coordinates of the display, and the fit of WMDS. We quantify these uncertainties using Bayesian models in a method we call Informative Probabilistic WMDS (IP-WMDS). Visually, we display estimated uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in WMDS. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts. Project 2: Analysis of network data has emerged as an active research area in statistics. Much of the focus of ongoing research has been on static networks that represent a single snapshot or aggregated historical data unchanging over time. However, most networks result from temporally-evolving systems that exhibit intrinsic dynamic behavior. Monitoring such temporally-varying networks to detect anomalous changes has applications in both social and physical sciences. In this work, we simulate data from models that rely on MDS, and we perform an evaluation study of the use of summary statistics for anomaly detection by incorporating principles from statistical process monitoring. In contrast to most previous studies, we deliberately incorporate temporal auto-correlation in our study. Other considerations in our comprehensive assessment include types and duration of anomaly, model type, and sparsity in temporally-evolving networks. We conclude that the use of summary statistics can be valuable tools for network monitoring and often perform better than more involved techniques. / Doctor of Philosophy / In this work, two main ideas in data visualization and anomaly detection in dynamic networks are further explored. For both ideas, a connecting theme is extensions of a method called Multidimensional Scaling (MDS). MDS is a dimension-reduction method that takes high-dimensional data (all $p$ dimensions) and creates a low-dimensional projection of the data. That is, relationships in a dataset with presumably a large number of dimensions or variables can be summarized into a lower number of, e.g., two, dimensions. For a given data, an analyst could use a scatterplot to observe the relationship between 2 variables initially. Then, by coloring points, changing the size of the points, or using different shapes for the points, perhaps another 3 to 4 more variables (in total around 7 variables) may be shown in the scatterplot. An advantage of MDS (or any dimension-reduction technique) is that relationships among the data can be viewed easily in a scatterplot regardless of the number of variables in the data. The interpretation of any MDS plot is that observations that are close together are relatively more similar than observations that are farther apart, i.e., proximity in the scatterplot indicates relative similarity. In the first project, we use a weighted version of MDS called Weighted Multidimensional Scaling (WMDS) where weights, which indicate a sense of importance, are placed on the variables of the data. The problem with any WMDS plot is that inaccuracies of the method are not included in the plot. For example, is an observation that appears to be an outlier, really an outlier? An analyst cannot confirm this without further context. Thus, we created a model to calculate, visualize, and interpret such inaccuracy or uncertainty in WMDS plots. Such modeling efforts help analysts facilitate exploratory data analysis. In the second project, the theme of MDS is extended to an application with dynamic networks. Dynamic networks are multiple snapshots of pairwise interactions (represented as edges) among a set of nodes (observations). Over time, changes may appear in some of the snapshots. We aim to detect such changes using a process monitoring approach on dynamic networks. Statistical monitoring approaches determine thresholds for in-control or expected behavior that are calculated from data with no signal. Then, the in-control thresholds are used to monitor newly collected data. We applied this approach on dynamic network data, and we utilized a detailed simulation study to better understand the performance of such monitoring. For the simulation study, data are generated from dynamic network models that use MDS. We found that monitoring summary statistics of the network were quite effective on data generated from these models. Thus, simple tools may be used as a first step to anomaly detection in dynamic networks.

The task of analyzing a collection of documents generated over time is daunting. A natural way to ease the task is by summarizing documents into the topics that exist within these documents. The temporal aspect of topics can frame relevance based on when topics are introduced and when topics stop being mentioned. It creates trends and patterns that can be traced by individual key terms taken from the corpus. If trends are being established, there must be a way to visualize them through the key terms. Creating a visual system to support this analysis can help users quickly gain insights from the data, significantly easing the burden from the original analysis technique. However, creating a visual system for terms is not easy. Work has been done to develop word embeddings, allowing researchers to treat words like any number. This makes it possible to create simple charts based on word embeddings like scatter plots. However, these methods are inefficient due to loss of effectiveness with multiple time slices and point overlap. A visualization method that addresses these problems while also visualizing diachronic word embeddings in an interesting way with added semantic meaning is hard to find. These problems are managed through TimeLink. TimeLink is proposed as a dashboard system to help users gain insights from the movement of diachronic word embeddings. It comprises a Sankey diagram showing the path of a selected key term to a cluster in a time period. This local cluster is also mapped to a global topic based on an original corpus of documents from which the key terms are drawn. On the dashboard, different tools are given to users to aid in a focused analysis, such as filtering key terms and emphasizing specific clusters. TimeLink provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time. / Master of Science / The task of analyzing documents collected over time is daunting. Grouping documents into topics can help frame relevancy based on when topics are introduced and hampered. The creation of topics also enables the ability to visualize trends and patterns. Creating a visual system to support this analysis can help users quickly gain insights from the data, significantly easing the burden from the original analysis technique of browsing individual documents. A visualization system for this analysis typically focuses on the terms that affect established topics. Some visualization methods, like scatter plots, implement this but can be inefficient due to loss of effectiveness as more data is introduced. TimeLink is proposed as a dashboard system to aid users in drawing insights from the development of terms over time. In addition to addressing problems in other visualizations, it visualizes the movement of terms intuitively and adds semantic meaning. TimeLink provides insightful visualizations focused on the movement of terms while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Project Meme Evolution Programme (Project MEP) is a research program directed by Raazesh Sainudiin, Uppsala University, Sweden, that collects and analyzes datasets from Twitter. Twitter can be used to understand how ideas spread in social media. This project aims to produce interactive visualizations for datasets collected in Project MEP. Such interactive visualizations will facilitate exploratory data analysis in Project MEP. Several technologies had to be learned to produce the visualizations, most notably JavaScript, D3, and Scala. Three interactive visualizations were produced; one that allows for exploration of a Twitter user timeline and two that allows for exploration and understanding of a Twitter retweet network. The interactive visualizations are accessible as Scala functions and in a website developed in this project and uploaded to GitHub. The interactive visulizations contain some known bugs but they still allow for useful exploratory data analysis of Project MEP datasets and the project goal is therefore considered met. / Project Meme Evolution Programme