Email has developed into one of the most extensively used computer applications. Email interfaces, on the other hand, have gone through very few transformations since their inception. As the growing volumes of email data accumulate in users' email boxes, these interfaces fail to provide effective message handling and browsing support. Saved email messages provide not only a vast and pulsating record of one's electronic past, but also a potential source of valuable insights into the structure and dynamics of one's social network. In this thesis, we introduce a visualization approach to email that draws upon email’s inherently personal character and uses intimacy as a key parameter. We have developed faMailiar, a novel email interface that visualizes email in a chronological manner through two alternative, calendar-like views that present email activity on different time granularity scales. Visual mappings of email data and support for filtering help the user see rhythms and patterns in her social interactions. Zooming, panning and implicit semantic zooming facilitate navigation across large email collections. This thesis also describes our iterative, human-centered design method. Two user studies have been performed at different stages of the process, and we explain their purpose, results and implications.

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. This dissertation is motivated by the observation that a general approach for performing multi-dimensional and multi-level network-based visual analysis on multivariate tabular data is necessary. We present a formal framework based on the relational data model that systematically specifies the construction and transformation of graphs from relational data tables. In the framework, a set of relational operators provide the basis for rich expressive power for network modeling. Powered by this relational algebraic framework, we design and implement a visual analytics system called Ploceus. Ploceus supports flexible construction and transformation of networks through a direct manipulation interface, and integrates dynamic network manipulation with visual exploration for a seamless analytic experience.

<p>In this thesis, a 3D approach to visualize software metrics is presented. Software metrics are attributes of a piece of software or its specification. They generally contain a set of multivariate time-series data and can be displayed, for example, as a Kiviat diagram consisting of axes and polylines. The aim of this work is to design a Win32 application that can load multivariate time-series data from a file and visualize it as an interactive 3D Kiviat diagram.</p><p>There has been an approach that can display software metrics by using 2D Kiviat diagrams, but there are still some drawbacks on it. Since a better visualization of software metrics can help the developer to control the quality of software products more easily, this thesis improved the existing approach by extending 2D Kiviat diagram to 3D Kiviat diagram.</p>

Thesis (Ph. D.)--Mississippi State University. Department of Computational Engineering. / Title from title screen. Includes bibliographical references.

Geo-based Information Visualizations (GIV) allow people to analyze data points based on their related geographic locations. This approach is usually adopted where a large scale geo-referenced dataset is present and users are trying to find a way to examine hidden patterns within this data. One of the emerging trends in GIV is to visualize social media data to show how information flows between users of popular social networking sites. Due to its public nature and the large number of users, most of the visualizations in this area rely on conversational data from Twitter (Twitter.com). In this thesis, we design and implement a web-based interactive GIV system, AcademiaMap-GIV, to visualize online conversations among scholars on Twitter. A formal exploratory user study was also conducted on the target users. The study results demonstrated that most of the study respondents found the features of AcademiaMap-GIV effective in regards to visualizing information of their interests.

Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine.

Model Driven Engineering (MDE) is an approach to software development by which software is specified, designed, implemented and deployed through a series of models. While the capabilities of MDE have been realized in many aspects of software development, there is no MDE supported technique for generating information visualizations. Information visualization is a technique that supports human cognition through interactive graphics by enabling users to identify data patterns more easily, summarize information or abstract concepts that are not easily comprehended from the underlying data. As more systems are designed using model driven engineering approaches there is now a need to support a model driven approach for creating such visualizations. This research explores the feasibility of a model driven approach to view creation that is compatible with the goals of MDE. We approach the problem of developing an MDE technique for view creation in two ways. First, we examine how MDE technologies are used for specifying, designing, and maintaining software systems to uncover the aspects of software customization that are supported through MDE. Second, we analyze six existing visualization tools to determine three functional requirements and six design recommendations for visualization creation and customization tools. Combining MDE principles and information visualization requirements, we propose Model Driven Visualization (MDV), a model based approach to view creation. MDV includes platform independent models for common visualizations, as well as a technique to generate platform specific instances of these models. Finally, using MDV we show that standard visualizations can be recreated in a concise syntax, that is compatible with the goals of model driven engineering. MDV contributes to the fields of model driven engineering, information visualization and software engineering. In particular, this research 1) provides a collection of formal view models for common information visualization techniques, 2) outlines a method for designing and customizing information visualizations using MDE, 3) presents a code generation technique for integrating MDE with the model-view-controller pattern, and 4) contributes an open-source visualization toolkit to the Eclipse project.

The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements.

A tremendous amount of information is produced in the world around us, both as a product of our daily lives and as artifacts of our everyday work. An emerging area of Human-Computer Interaction (HCI) focuses on helping individuals manage this flood of information. Prior research shows that multiple displays can improve an individual user's ability to deal with large amounts of information, but it is unclear whether these advantages extend for teams of people. This is particularly relevant as more employees are spending large portions of their workdays in meetings My contribution to HCI research is empirical fieldwork and laboratory studies investigating how multiple shared displays improve aspects of teamwork. In particular, I present an insight-based evaluation method for analyzing how teams collaborate on a data-intensive sensemaking task. Using this method, I show how the presence and location of multiple shared displays impacted the meeting process with respect to performance, collaboration, and satisfaction. I also illustrate how multiple shared displays engaged team members who might not have otherwise contributed to the collaboration process. Finally, I present Mimosa, a software tool developed to visualize large volumes of time series data. Mimosa combines aspects of information visualization with data analysis, facilitating a deep and iterative exploration of relationships within large datasets.

Information visualization can be valuable in a wide range of applications, it deals with abstract, non-spatial data and with the representation of data elements in a meaningful form irrespective of the size of the data, because sometimes visualization itself focuses on the certain key aspects of the data in the representation and thus it helps by providing ease for the goal oriented interpretation. Information visualization focuses on providing a spontaneous and deeper level of the understanding of the data. Research collaboration enhances sharing knowledge and also enhances an individual’s talent. New ideas are generated when knowledge is shared and transferred among each other. According to (He et al, 2009) Research collaboration has been considered as a phenomenon of growing importance for the researchers, also it should be encouraged and is considered to be a “good thing” among the researchers. The main purpose of this thesis work is to prepare a model for the competence profile visualization purpose. For this purpose the study of different visualization techniques that exist in the field of information visualization are discussed in this thesis work. The study and discussion about the visualization techniques motivates in selecting appropriate visualization techniques for the visualization of Ontology-based competence profiles for research collaboration purpose. A proof of concept is developed which shows how these visualization techniques are applied to visualize several components of competence profile.