Doctor of Philosophy, Case Western Reserve University, 2008, Management Information and Decision Systems

Information is often multidimensional and dynamic, which makes it difficult to communicate using traditional representations such as verbal descriptions or even graphics. However, the development of advanced visualization technology allows the creation of more effective representations. By taking a distributed cognition perspective and integrating several theories of visualization, this study formulated a theoretical model to examine the effects of information representation on a classic business decision-making task: bankruptcy prediction. The use of an animated visualization to represent dynamic information was compared with conventional tables and graphs. Findings from accounting and information systems literature were used to guide the design of the animated representation, Business Animator. A laboratory experiment using factorial design was conducted to test the model, with participants being randomly assigned to view a static representation (table or graph) or Business Animator, and then to make bankruptcy predictions for thirteen firms. Accuracy and speed were measured. The results showed that animated representations were more accurate than static representations with the advantage being unrelated to the subject's level of domain knowledge; however, they were not significantly superior in terms of speed. Post hoc interviews indicated the subjects preferred the animations but also suggested potential changes in the design of Business Animator. The findings indicated that animation facilitated identification of the flows and problems in operating and financing processes, thereby improving the subjects' assessment of firm health.

Committee: Fred Collopy (Advisor) Subjects: Business Administration, Management

PHD, Kent State University, 2020, College of Arts and Sciences / Department of Computer Science

Data containing geographical locations and time that associates with natural language texts, such as geotagged tweets, travel blogs, and crime reports are generally recognized as spatiotemporal semantic events. Many research fields have tried to gain valuable insights from these data and there have many techniques and methods are introduced in past decade. In computer science field, the study of spatiotemporal-semantic events in visualization and visual analytics is one of the hottest research topics. Text mining and data mining provide abundant methods to find meaningful knowledge and insights from semantic information of these data. Even though, there exist many contributions in this research field, there still lack of visually intuitive applications and approaches that allow frontline users, such as police, health officers, and social workers to freely navigate, effectively utilize and analyze their spatiotemporal semantic data, especially in community level. In this thesis, multiple visual analytics (VA) solutions are introduced. NeighborVis, CLEVis, and a new lens based visual interaction technique, GTMapLens to help frontline users harness semantic-rich spatiotemporal data. The development of all applications is fulfilled the requirement analysis and initial prototype evaluation. Text mining, topic modeling, hierarchical geospatial data indexing and many new visualization methods are studied and discussed along with those VA systems. The visual design is guided by requirement analysis with a cohort of multidisciplinary domain experts. Evaluation is presented with real world datasets to show the usability and effectiveness.

Committee: Ye Zhao (Committee Chair); Xiang Lian (Committee Member); Jong-Hoon Kim (Committee Member); Xinyue Ye (Committee Member); Jay Lee (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2019, Computer Science and Engineering

Recent advancements in the field of computational sciences and high-performance computing have enabled scientists to design high-resolution computational models to simulate various real-world physical phenomenon. In order to gain key scientific insights about the underlying phenomena it is important to analyze and visualize the output data produced by such simulations. However, large-scale scientific simulations often produce output data whose size can range from a few hundred gigabytes to the scale of terabytes or even petabytes. Analyzing and visualizing such large-scale simulation data is not trivial. Moreover, scientific datasets are often multifaceted (multivariate, multi-run, multi-resolution, etc.), which can introduce additional complexities to the analyses and visualization activities. This dissertation addresses three broad categories of data analysis and visualization challenges: (i) multivariate distribution-based data summarization, (ii) uncertain analysis in ensemble simulation data, and (iii) simulation parameter analysis and exploration. We proposed statistical and machine learning-based approaches to overcome these challenges. A common strategy to deal with large-scale simulation data is to partition the simulation domain and create data summaries in the form of statistical probability distributions. Instead of storing high-resolution raw data, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck issues. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. Therefore, we proposed a flexible copula-based multivariate distribution modeling strategy to create multivariate data summaries during simulation execution time (i.e, in-situ data modeling). The resulting data summaries can be subsequently used to perform scalable post-hoc analysis and visualization. In many cases, scientists execute their simulations mu (open full item for complete abstract)

Committee: Han-Wei Shen (Advisor); Rephael Wenger (Committee Member); Yusu Wang (Committee Member) Subjects: Computer Science; Statistics

PHD, Kent State University, 2019, College of Arts and Sciences / Department of Computer Science

Advanced sensing technologies and computing infrastructures have produced a variety of trajectory data of moving objects in urban spaces. One type of this data is taxi trajectory data. It records real-time moving paths sampled as a series of positions associated with vehicle attributes over urban road networks. Such data is big, spatial, temporal, unstructured and it contains abundant knowledge about a city and its citizens. Exploratory visualization systems are needed to study taxi trajectories with efficient user interaction and instant visual feedback. The extracted information can be utilized in many important and practical applications to optimize urban planning, improve human life quality and environment. As the primary novelty contribution, this thesis presents a set of visual analytics solutions with different approaches to interacting with massive taxi trajectory data to allow analysts to look at the data from different perspectives and complete different analytical tasks. Our approaches focus on how people directly interact with the data store, query and visualize the results and support practitioners, researchers, and decision-makers to advance transportation and urban studies in the new era of the smart city. First, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps global positioning system (GPS) points into a series of street/POI names and pickup/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Second, we present a visual analytics system, named as QuteVis, which facilitates domain users to query and examine traffic patterns from large-scale traffic data in an urban transpor (open full item for complete abstract)

Committee: Ye Zhao (Committee Chair); Cheng-Chang Lu (Committee Member); Xiang Lian (Committee Member); Xinyue Ye (Committee Member); Xiaoling Pu (Committee Member) Subjects: Computer Science

PHD, Kent State University, 2019, College of Arts and Sciences / Department of Computer Science

Massive urban trajectories of humans and vehicles, together with road network and Points of Interest (POIs), have been used in a wide range of research by transportation engineers and urban planning professionals. This has contributed to improve urban planning, transportation management, and knowledge of human dynamics. Interactive visual analytics tools allow a variety of users to conduct iterative visual studies over the big data with intuitive visual representations and convenient interactions. Typically, the visual analytics tasks should be conducted in three main phases: (1) Preprocessing and preparing raw trajectory data with cleaning, enrichment, aggregation, and transformations. (2) Developing efficient data structures and query operations to support interactive visual querying and analysis over big data. (3) Designing visual interface with effective and convenient human-computer interactions. Firstly, this dissertation develops data preprocessing tools of various trajectories, road networks, and POIs, which can be directly used by general users through a web-based system. Users can directly upload raw trajectory data, while the system automatically fetches corresponding road segments data from OpenStreetMap (OSM), extracts zip code regions, or creates grid rectangular regions to couple the raw GPS data with geographical context. The system also automatically matches the trajectories with these road segments or regions. Secondly, effective data models are designed to store and manage heterogeneous urban data in a spatial database called the Trajectory DataBase (TrajBase). The key contribution is to develop trajectories and road segments (or regions) based geo-indexing scheme for trajectory-based urban study. This scheme can support fast spatial-temporal queries and visualization, while the traditional geo-indexing scheme is mostly designed for point-based geo-data. Thirdly, based on the proposed data models and tools, visual analytics queries and functions a (open full item for complete abstract)

Committee: Ye Zhao Dr. (Advisor); Feodor Dragan Dr. (Committee Member); Arden Ruttan Dr. (Committee Member); Xinyue Ye Dr. (Committee Member); Wei Li Dr. (Committee Member) Subjects: Computer Science

Bachelor of Business Administration (BBA), Ohio University, 2018, Business Administration

This paper reviews prominent authors in the field of data visualization in order to gain a deeper understanding of the process of creating successful data visualizations. Business, journalism, and design implications are taken into consideration. A new set of guidelines for all steps of the data visualization process is proposed that can be used across disciplines and levels of understanding. A data visualization series is presented along with explanation of the application of these guidelines in the project. Practitioners and researchers across fields will have a deeper understanding of data visualization and be able to apply the guidelines to more successfully communicate with their audiences.

Committee: Raymond Frost (Advisor) Subjects: Business Administration; Design

Doctor of Philosophy, Case Western Reserve University, 2017, EECS - Computer Engineering

In this era of technological transformation in medicine, there is need to revolutionize the approach and procedures involved in the treatment of diseases to have a restructured understanding of the role of data and technology in the medical industry. Data is a key factor in diagnosis, management, and treatment of patients in any medical institution. Proper management and usage of patient's data will go a long way in helping the society save money, time and life of the patient. Having data is one thing and providing a system or means of translating the data is another issue. This dissertation is proposing a design of a Point of Care system for the Intensive Care Unit (a.k.a ICU_POC), which is a system that integrates the capabilities of the bedside monitors, bedside eFlowsheet and the Electronic Medical Records in such a manner that the clinicians interact with one another in real time from different locations, to view, analyze, and even make necessary diagnoses on patients' ailment based on their medical records. It demonstrates how patient data from the monitors can be imported, processed, and transformed into meaningful and useful information, stored, reproduced and transferred automatically to all necessary locations securely and efficiently without any human manipulation. ICU_POC will grant physicians the remote capability in managing patients properly by providing accurate patient data, easy analysis and fast diagnosis of patient conditions. It creates an interface for physicians to query historical data and make proper assumptions based on previous medical conditions. The problem lies in managing data transfer securely between one hospital EMR database and the other for easy accessibility of data by the physicians. This work is challenged by designing a system that could provide a fast, accurate, secure and effective (FASE) diagnosis of medical conditions of the patients in the ICU. The proposed system has the potential of reducing patients' length of stay i (open full item for complete abstract)

Committee: Kenneth Loparo (Advisor); Farhad Kaffashi (Committee Member); Vira Chankong (Committee Member); Michael Degeorgia (Committee Member) Subjects: Computer Engineering; Computer Science; Engineering

Master of Science, The Ohio State University, 2017, Civil Engineering

Building Information Modeling (BIM) is an effective tool which widely used in construction industries. As its result, building information models (BIMs) serve an important role through project design, delivery, build and management stages, bringing many benefits. There are many reliable commercial BIM software on market using computers as their main platform. But the way they display the BIM and interact with the BIM are also limited by computers. On the other hand, Augmented Reality (AR), as a latest popular technique, shows a great potential of changing the way of people observing and interacting with the world. It provides a seamless way of combing virtual digital contents with the real world. In this paper, we will discuss about the development of BIM and AR technique, and the possible benefits of combing them. In the last chapter we present an experimental system that is able to visualize BIM in AR. The results are demonstrated and the whole idea of our system can be served as a general framework of a wider range of AR-BIM system development.

Committee: Alper Yilmaz Dr. (Advisor); Wang Lei Dr. (Committee Member); Qin Rongjun Dr. (Committee Member) Subjects: Civil Engineering

PHD, Kent State University, 2016, College of Arts and Sciences / Department of Computer Science

Graph models can represent a variety of data types such as social media, cyber business and security, web, urban networks, and more. They are extensively studied and widely used in data management, mining, and analysis in many important application areas. On the other hand, graph visualization has been a major topic in information visualization to manifest graph structure and features for effective and intuitive data exploration. In this thesis, we present a set of visual analytics solutions for several important applications by integrating graph models with visualization tools, including the visualization systems of urban trajectory data, text stream data, and categorical data. Our approaches utilize graphs to abstract and manage various data, to discover hidden knowledge with graph algorithms, and to help users gain insights from graph-based visualizations and interaction. Our research widens the horizon and enhances the capability of visual analytics methodologies. First, we propose a new visual analytics method, TrajGraph, for studying urban mobility patterns. In particular, a graph model represents taxi trajectories traveling over road networks. Then graph computation is applied to identify graph centralities that find the time varying hubs and backbones of road networks from massive taxi trajectories. The graph is further visualized and interacted for users to explore the important roles of city streets and regions. Second, we employed a parallel-graph model to enhance visual analytics of the large-scale urban trajectory datasets. Specifically, we designed a novel, scalable parallel-graph model for trajectory data management. It supports fast computation over various information queries in distributed environments. A new visualization tool that allows users to get statistics information, and relationship of cars and roads in the big trajectory data by employing the functionalities of the parallel-graph model. Third, we develop a dynamic visualizati (open full item for complete abstract)

Committee: Ye Zhao (Advisor); Ruoming Jin (Committee Member); Chengchang Lu (Committee Member); Xinyue Ye (Committee Member); Donald White (Committee Member) Subjects: Computer Science

Doctor of Business Administration, Cleveland State University, 2014, Monte Ahuja College of Business

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation's impact on users' cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practice.

Committee: Raymond Henry PhD (Committee Chair); Radha Appan PhD (Committee Member); Amit Ghosh PhD (Committee Member); Robert Whitbred PhD (Committee Member) Subjects: Information Systems; Information Technology

Master of Computing and Information Systems, Youngstown State University, 2008, Department of Computer Science and Information Systems

In this paper, we explore the concept of epistemic visualization in interrogative domains. Epistemic visualization is the process and result of developing visual models that capture the structure, content, justification and acquisition of knowledge obtained by a software agent in a knowledge-based system. The knowledge is the foundation in which the agent can respond to queries against a corpus containing questions and answers. The visualizations are therefore used to examine the quality of the software agent's knowledge. The visual models will include justification and commitment artifacts as well as knowledge acquisition flow. The visualization will demarcate the a priori and posteriori knowledge. The knowledge of the software agent is stored in epistemic structures which are knowledge representation schemes that supports the basic concepts of knowledge as defined by the tripartite analysis of knowledge. Epistemic visualization is used to analyze the quality of the knowledge of a software agent in an interrogative domain. For our purpose, interrogative domains are hearings, trials, interrogations, personality test or any document source in which the primary content is questions and answers pairs. In this paper, we introduce the Epistemic Structure Es that captures the agent's knowledge and the visualization of that epistemic structure using common visualization techniques.

Committee: Alina Lazar PhD (Committee Chair); John Sullins PhD (Committee Member); Yong Zhang PhD (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Systems; Linguistics; Technology

MDes, University of Cincinnati, 2004, Design, Architecture, Art, and Planning : Design

Information access for the help sub-systems of software applications has traditionally focused on text-based, on-line systems, using a book-type format with its table of contents and indices as the primary means of assisting users in performing the specific tasks required for finding the information they need. Such systems are often inadequate for allowing users, especially those who are graphically oriented, to access the targeted information. Most incomplete tasks are the results of tedious searching or losing context in navigation. This thesis presents research in the application of information visualization techniques to the problem of navigating and finding information in help sub-systems built within software applications. It provides a methodology for graphically presenting detailed information about a specific topic in an interactive way to accommodate the users who are graphically oriented while also presenting a complete overview of all the information available. A prototype has been developed for visualization of the help sub-system of Adobe Illustrator 10. Limitations of that prototype and future direction of work have also been discussed.

Committee: Marty Plumbo (Advisor) Subjects: Information Science

Master of Science, The Ohio State University, 2011, Computer Science and Engineering

In this thesis, we attempt to propose a strategy for discovering the contents people are sharing inside Twitter using information visualization. Twitter is one of the most popular social networking services nowadays which grows dramatically in recent years. These services such as Twitter, Facebook, and MySpace are becoming more and more important in our lives. People share information by text, image, video, etc. Thus, by analyzing social network data, meaningful information can be discovered, such as popular topics users are discussing, and trends of important events. But, the result generated by data analysis is not easy for people to interpret directly. Information visualization then becomes a key to assist user in interpreting data. The primary goal of this work is to visualize time-varying Twitter text data by word cloud. While the word cloud has widely been used to visualize text data, to visualize time-varying text data there still exist many additional challenges. Therefore, we introduce an animation-based dynamic time-varying word clouds design to address this problem. We first propose a circular word cloud layout to provide users an overview of the time-varying data content. Based on this layout, we propose animation methods to assist users in interpreting the property of time-vary data. The animated word clouds preserve the context while the focus is changing. Thus, the visualization not only provides an overview of huge time-varying Twitter text data, but also assists users in identifying the changing of content from time to time. Finally, two case studies are provided. One is the visualization of a long term event, and the other is the visualization of a series of short term events people in Twitter were discussing.

Committee: Han-Wei Shen PhD (Advisor); Richard Parent PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2011, Computer Science and Engineering

As the speed of computers continues to increase at a very fast rate, the size of data generated from scientific simulations has now reached petabytes ($10^{12}$ bytes) and beyond. Under such circumstances, no existing techniques can be used to perform effective data analysis at a full precision. To analyze large scale data sets, visual analytics techniques with effective summarization and flexible interface are crucial in assisting the exploration of data at different levels of detail. To improve data access efficiency, summarization and triage are important components for categorizing data items according to their saliency. This will allow the user to focus only on the relevant portion of data. In this dissertation, several visualization and analysis techniques are presented to facilitate the analysis of multivariate time-varying data and flow fields. For multivariate time-varying data sets, data items are categorized based on the values over time to provide an effective overview of the time-varying phenomena. From the similarity to the user-specified feature, dynamic phenomena across multiple variables in different spatial and temporal domains can be explored. To visualize flow fields, information theory is used to model the local flow complexity quantitatively. Based on the model, an information-aware visualization framework is designed to create images with different levels of visual focus according to the local flow complexity. By extending the measurement from object space to image space, visualization primitives can be further rearranged, leading to more effective visualization of salient flow features with less occlusion.

Committee: Han-Wei Shen PhD (Advisor); Roger A. Crawfis PhD (Committee Chair); Raghu Machiraju PhD (Committee Chair) Subjects: Computer Science

Master of Science, The Ohio State University, 2011, Computer Science and Engineering

In this thesis, a web-based interface to visualize the spatial and temporal property of hydrological data for general public is proposed and developed. The prototype interface provides non-expert users a user-friendly and intuitive environment to browse, explore and understand the hydrological data from the SWOT (Surface Water and Ocean Topography) satellite mission proposed by NASA. The prototype interface is based on Google Earth, a virtual globe program with high capability in visualizing spatial data. To achieve the goal to simultaneously visualize the spatial and temporal property of hydrological data, an mechanism to integrate Google Earth plugin/API with database and data visualization tools is proposed and implemented to provide user with interactive visualization for geospatial data and time series charts. The time series of certain location can be fetched from database and visualized in the prototype interface. A focus + context visualization technique is also used to visualize time series data, which gives users the overview of the whole area and emphasizes the detail users interested in. Furthermore, comparative visualization is supported to facilitate the time series comparison among different positions.

Committee: Han-Wei Shen PhD (Advisor); Dong Xuan PhD (Committee Member) Subjects: Computer Science

Master of Computer Science, Miami University, 2011, Computer Science and Software Engineering

The Poetess Archive at Miami University includes a database of electronic documents encoded using the TEI (Text Encoding Initiative) schema extended with Poetess Archive tag-set, and derived from widely used terms in literary analysis and criticism. The extended TEI schemas based on the Xml standards hold elements of interest to a literary scholar, and are spread across multiple encodings. The Xml representation of literary texts is suitable for machine processing and electronic exchange of information, but does little to promote adoption and intuitive use of these resources by scholars. We are developing a visualization tool that seeks to integrate multiple encodings while allowing comparative analysis of multiple poems encoded using the extended TEI tag-set. The proposed solution is an interactive visual representation of differently encoded versions of text that can enhance cognition, and aid in uncovering of new knowledge. This approach will facilitate identification of frequently changing hotspots in encoded text and aid in the process of close reading.

Committee: Gerald Gannod PhD (Advisor); Laura Mandell PhD (Committee Member); Alton Sanders PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2013, EDU Policy and Leadership

Spatial ability is a cognitive skill required for success in many professions. Those in the Allied Medical professions utilize this skill in the performance of many of their daily tasks. Understanding the nature of this ability in Allied Medical educational programs may allow educators to improve the delivery of material or develop training material for students who have low levels of spatial ability. In order to assess spatial ability in Allied Medical students, valid instruments are needed. This study used the argument-based approach to examine the validity evidence for six tests of spatial ability in a group of 128 Allied Medical students. Tests were chosen to assess spatial perception (Cube Comparison Test and The Purdue Spatial Visualization Test Visualization of Views), spatial visualization (Hidden Figures Test and The Purdue Spatial Visualization Test Visualization of Developments), and mental rotation (Mental Rotations Test and The Purdue Spatial Visualization Test Visualization of Rotations). Evidence is presented to support the assertions that some of these tests can be interpreted as spatial ability measures, but the assertions that the chosen tests measure spatial perception, spatial visualization, and mental rotation abilities was not satisfied.

Committee: Dorinda Gallant (Advisor) Subjects: Curriculum Development; Education; Educational Evaluation; Educational Tests and Measurements; Health Education; Psychological Tests

Doctor of Philosophy, The Ohio State University, 2013, Computer Science and Engineering

Information visualization, a field that studies visual representations of abstract data where no spatial representation is available, has been playing an essential role in assisting people to understand the vast amount of information created by modern technology. Visualizing large complex structured data is an important area as the structured data are ubiquitous in many aspects of our lives. The large size, high complexity, and vast variety in user interests pose formidable challenges to create effective representations for those structured data. To help users understand detailed information in the large dataset based on their changing interests, several focus-based interactive visualization methods have been described. To allow users to discover specific contextual information around the focus in large semantic graphs, we propose to use the embedded semantic queries during browsing as the main method for information discovery. In addition, to let users quickly understand the different aspects of the graph data, we propose to set up multiple contexts and enable users to quickly switch among the contexts without any abrupt layout changes. Moreover, to assist users in quickly identifying the focal entities when comparing two treemaps, we propose novel contrast techniques to highlight the key differences of the two treemaps in the context of a single treemap so that direct comparison can be done easily. Furthermore, to facilitate the study of the details of multiple foci in a treemap, we propose a focus+context technique to seamlessly enlarge multiple foci in the same view while maintaining a consistent and stable layout. The effectiveness of these approaches are evaluated by case studies and user studies, where we have clearly demonstrated that users can better understand the structured data with more details and in less amount of time. Both free exploration and task-oriented scenarios were studied in our experiments.

Committee: Han-Wei Shen (Advisor); Roger Crawfis (Committee Member); Richard Parent (Committee Member) Subjects: Computer Science