"Visual analytics is an emerging field which uses visual techniques to interact with users in the analytical reasoning process. Users can choose the most appropriate representation that conveys the important content of their data by acting upon different visual displays. The data itself has many features of interest, including clusters, trends (commonalities) and anomalies. Most visualization techniques currently focus on the discovery of trends and other relations, where uncommon phenomena are treated as outliers and are either removed from the datasets or de-emphasized on the visual displays. Much less work has been done on the visual analysis of outliers, or anomalies. In this thesis, I will introduce a method to identify the different levels of “outlierness” by using interactive selection and other approaches to process outliers after detection. In one approach, the values of these outliers will be estimated from the values of their k-Nearest Neighbors and replaced to increase the consistency of the whole dataset. Other approaches will leave users with the choice of removing the outliers from the graphs or highlighting the unusual patterns on the graphs if points of interest lie in these anomalous regions. I will develop and test these anomaly handling methods within the XMDV Tool."

Approximate query processing (AQP) provides fewer representative samples to approximate large amounts of data. Processing these smaller data subsets enables visualization systems to provide end-users with real-time responses. However, challenges arise for real-world users in adopting AQP-based visualization systems, e.g., the absence of AQP modules in mainstream commercial databases, erroneous estimations caused by sampling bias, and end-user uncertainty when interpreting approximate query results. In this dissertation, we present an AQP-centered technique for enabling interactive visual analytics for large amounts of spatiotemporal data under the aforementioned challenges. First, we design, implement and evaluate a client-based visual analytics framework that progressively acquires spatiotemporal data from an AQP-absence server-side to client-based visualization systems so that interactive data exploration can be maintained on a client machine with modest computational power. Second, we design, implement, and evaluate an online sampling approach that selects samples of large spatiotemporal data in an unbiased manner and accordingly improves the accuracy of the associated estimates. Last, we design, implement and evaluate a difference assessment approach that compares approximate and exact spatial heatmap visualizations in terms of human perception. As such, information changes perceptible by users are well represented, and users can evaluate the reliability of approximate answers more easily. Our results show the superior performance of our proposed AQP-centered technique in terms of speed, accuracy, and user trust, as compared to a baseline of state-of-the-art solutions.

Sensemaking (i.e. the process of deriving meaning from complex information to make decisions) is often cited as an important and challenging activity for collaborative technology. A key element to the success of collaborative sensemaking is effective coordination and communication within the team. It requires team members to divide the task load, communicate findings and discuss the results. Sensemaking is one of the human activities involved in visual analytics (i.e. the science of analytical reasoning facilitated by interactive visual interfaces). The inherent complexity of the sensemaking process imposes many challenges for designers. Therefore, providing effective tool support for collaborative sensemaking is a multifaceted and complex problem. Such tools should provide support for visualization as well as communication and coordination. Analysts need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate work activities amongst members of the team. Sharing externalizations (i.e. any information related to the course of analysis such as insights, hypotheses, to-do lists, reminders, etc recorded in the form of note/ annotation) could increase awareness and assist team members to better communicate and coordinate their work activities. However, we currently know very little about how to provide tool support for this sort of sharing. This thesis is structured around three major phases. It consists of a series of studies to better understand collaborative Visual Analytics (VA) processes and challenges, and empirically evaluate design ideas for supporting collaborative sensemaking. I investigate how collaborative sensemaking can be supported during visual analytics by a small team of collocated analysts. In the first phase of this research, I conducted an observational study to better understand the process of sensemaking during collaborative visual analytics as well as identify challenges and further requirements. This study enabled me to develop a deeper understanding of the collocated collaborative visual analytics process and activities involved. I found that record-keeping plays a critical role in the overall process of collaborative visual analytics. Record-keeping involves recording any information related to the analysis task including visualization snapshots, system states, notes, annotations and any other material for further analysis such as reminders and to-do lists. Based on my observations, I proposed a characterization of activities during collaborative visual analytics that encompasses record-keeping as one of the main activities. In addition, I characterized notes according to their content, scope, and usage, and described how they fit into a process of collaborative data analysis. Then, I derived guidelines to improve the design of record-keeping functionality for collocated collaborative visual analytics tools. One of the main design implications of my observational study was to integrate record-keeping functionality into a collaborative visual analytics tool. In order to examine how this feature should be integrated with current VA tools, in the second phase of this research, I designed, developed and evaluated a tool, CoSpaces (Collaborative Spaces), tailor-made for collocated collaborative data analysis on large interactive surfaces. Based on the result of a user study with this tool, I characterized users' actions on visual record-keeping as well as their key intentions for each action. In addition, I proposed further design guidelines such as providing various views of recorded material, showing manually saved rather than automatically saved items by default, enabling people to review collaborators' work unobtrusively, and automatically recommending items related to a user's analytical task. In the third phase, I took supporting record-keeping activities in the context of collaborative sensemaking a step further to investigate how this support should be designed to facilitate collaboration. To this end, I explored how automatic discovery and linking of common work can be employed within a ``collaborative thinking space'' (i.e. a space to enable analysts to record and organize findings, evidence, and hypotheses, also facilitate the process of sharing findings amongst collaborators), to facilitate synchronous collaborative sensemaking activities in visual analytics. The main goal of this phase was to provide an environment for analysts to record, organize, share and connect externalizations. I expected that this would increase awareness among team members and in turn would enhance communication and coordination of activities. I designed, implemented and evaluated a new tool, CLIP (Collaborative Intelligence Pad), that extends earlier thinking spaces by integrating new features that reveal relationships between collaborators' findings. Comparing CLIP versus a baseline tool demonstrated that linking collaborators' work led to significant improvement in analytical outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. Based on this study, I proposed design guidelines collaborative VA tools. In summary, I contribute an understanding for how analysts use VA tools during collocated collaboration. Through a series of observational user studies, I investigated how we can better support this complex process. More specifically, I empirically studied recording and sharing of analytical results. For this purpose, I implemented and evaluated two systems to be able to understand the effects of these tools on collaboration mechanics. These user studies along with various literature surveys on each specific topic resulted in a collection of guidelines for supporting and sharing externalizations. In addition, I proposed and evaluated several mechanisms to increase awareness among team members, resulting in more effective coordination and communication during the collaborative sensemaking process. The most novel contributions of this research are the identification and subsequent characterization of note taking behaviours as an important component of visual data exploration and analysis. Moreover, the design and evaluation of CLIP, providing preliminary evidence in support of automatically identifying and presenting relationships between collaborators' findings. / Graduate / 0984 / narges.mahyar@gmail.com

abstract: Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

Thesis (Ph. D.)--Washington State University, December 2007. / Includes bibliographical references (p. 98-107).

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

abstract: Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled. This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

Indiana University-Purdue University Indianapolis (IUPUI) / You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications.

User interaction in visual analytic systems is critical to enabling visual data exploration. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. For example, two-dimensional layouts of high-dimensional data can be generated by dimension reduction models, and provide users with an overview of the relationships between information. However, exploring such spatializations can require expertise with the internal mechanisms and parameters of these models. The core contribution of this work is semantic interaction, capable of steering such models without requiring expertise in dimension reduction models, but instead leveraging the domain expertise of the user. Semantic interaction infers the analytical reasoning of the user with model updates, steering the dimension reduction model for visual data exploration. As such, it is an approach to user interaction that leverages interactions designed for synthesis, and couples them with the underlying mathematical model to provide computational support for foraging. As a result, semantic interaction performs incremental model learning to enable synergy between the user's insights and the mathematical model. The contributions of this work are organized by providing a description of the principles of semantic interaction, providing design guidelines through the development of a visual analytic prototype, ForceSPIRE, and the evaluation of the impact of semantic interaction on the analytic process. The positive results of semantic interaction open a fundamentally new design space for designing user interactions in visual analytic systems. This research was funded in part by the National Science Foundation, CCF-0937071 and CCF-0937133, the Institute for Critical Technology and Applied Science at Virginia Tech, and the National Geospatial-Intelligence Agency contract #HMI1582-05-1-2001. / Ph. D.