Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 104-109).

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) / This study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. For interactive machine learning, we propose a new visual analytics approach to interactive machine learning. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building.

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.

Narratives are fundamental to our understanding of the world, and they are pervasive in all activities that involve representing events in time. Narrative analysis has a series of applications in computational journalism, intelligence analysis, and misinformation modeling. In particular, narratives are a key element of the sensemaking process of analysts. In this work, we propose a narrative model and visualization method to aid analysts with this process. In particular, we propose the narrative maps framework—an event-based representation that uses a directed acyclic graph to represent the narrative structure—and a series of empirically defined design guidelines for map construction obtained from a user study. Furthermore, our narrative extraction pipeline is based on maximizing coherence—modeled as a function of surface text similarity and topical similarity—subject to coverage—modeled through topical clusters—and structural constraints through the use of linear programming optimization. For the purposes of our evaluation, we focus on the news narrative domain and showcase the capabilities of our model through several case studies and user evaluations. Moreover, we augment the narrative maps framework with interactive AI techniques—using semantic interaction and explainable AI—to create an interactive narrative model that is capable of learning from user interactions to customize the narrative model based on the user's needs and providing explanations for each core component of the narrative model. Throughout this process, we propose a general framework for interactive AI that can handle similar models to narrative maps—that is, models that mix continuous low-level representations (e.g., dimensionality reduction) with more abstract high-level discrete structures (e.g., graphs). Finally, we evaluate our proposed framework through an insight-based user study. In particular, we perform a quantitative and qualitative assessment of the behavior of users and explore their cognitive strategies, including how they use the explainable AI and semantic interaction capabilities of our system. Our evaluation shows that our proposed interactive AI framework for narrative maps is capable of aiding users in finding more insights from data when compared to the baseline. / Doctor of Philosophy / Narratives are essential to how we understand the world. They help us make sense of events that happen over time. This research focuses on developing a method to assist people, like journalists and analysts, in understanding complex information. To do this, we introduce a new approach called narrative maps. This model allows us to extract and visualize stories from text data. To improve our model, we use interactive artificial intelligence techniques. These techniques allow our model to learn from user feedback and be customized to fit different needs. We also use these methods to explain how the model works, so users can understand it better. We evaluate our approach by studying how users interact with it when doing a task with news stories. We consider how useful the system is in helping users gain insights. Our results show that our method aids users in finding important insights compared to traditional methods.

Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms. / Master of Science / Data scientists are interested in developing visual analytics notebooks. However, different notebook platforms have different support for visual analytics components, such as visualizations and user interactions. To investigate which notebook platform to use for visual analytics, we built notebooks based on three different notebook platforms, i.e., Jupyter Notebook (with Python), Observable Notebook (with JavaScript), and Jupyter Notebook (with Python and JavaScript). Based on the implementation and user interactions, we explained why significant differences exist via specific metrics, such as programming difficulty, notebook organization, interactive performance, and the UI design choice. Furthermore, our work will benefit future researchers in choosing suitable notebook platforms for implementing visual analytics notebooks.

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.

Big Data is transforming the way we live. From medical care to social networks, data is playing a central role in various applications. As the volume and dimensionality of datasets keeps growing, designing effective data analytics algorithms emerges as an important research topic in statistics. In this dissertation, I will summarize our research on two data analytics algorithms: a visual analytics algorithm named Grouped Observation Level Interaction with Multidimensional Scaling and a big data Monte Carlo sampling algorithm named Batched Permutation Sampler. These two algorithms are designed to enhance the capability of generating meaningful insights and utilizing massive datasets, respectively. / Ph. D.

Learning from text data often involves a loop of tasks that iterate between foraging for information and synthesizing it in incremental hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the tasks at hand. To address this issue, we developed the multi-model semantic interaction (MSI) technique, which leverages user interactions to aid in the display layout (as was seen in previous semantic interaction work), forage for new, relevant documents as implied by the interactions, and then place them in context of the user's existing spatial layout. This results in the ability for the user to conduct both implicit queries and traditional explicit searches. A comparative user study of StarSPIRE discovered that while adding implicit querying did not impact the quality of the foraging, it enabled users to 1) synthesize more information than users with only explicit querying, 2) externalize more hypotheses, 3) complete more synthesis-related semantic interactions. Also, 18% of relevant documents were found by implicitly generated queries when given the option. StarSPIRE has also been integrated with web-based search engines, allowing users to work across vastly different levels of data scale to complete exploratory data analysis tasks (e.g. literature review, investigative journalism). The core contribution of this work is multi-model semantic interaction (MSI) for usable big data analytics. This work has expanded the understanding of how user interactions can be interpreted and mapped to underlying models to steer multiple algorithms simultaneously and at varying levels of data scale. This is represented in an extendable multi-model semantic interaction pipeline. The lessons learned from this dissertation work can be applied to other visual analytics systems, promoting direct manipulation of the data in context of the visualization rather than tweaking algorithmic parameters and creating usable and intuitive interfaces for big data analytics. / Ph. D.