The thesis addresses two related problems. It is argued that the materiality of physical artifacts serves the purpose of expressing abstract information. In contrast, the intangibility of IT is of such a kind that it poses different conditions for expressing abstract information. The background problem concerns conditions and possibilities of designing information visualization artifacts that retain the experiential qualities typically associated with physical artifacts. Massification design is introduced as a design ideal that aims towards a design of visualization artifacts that cater to the need of intersubjective understanding of abstract and intangible information. Massification design is further articulated as an ideal where the designed artifact as such bears witness of its own meaning. This ideal is put in contrast to design that depends on arbitrary, interpretative conventions for people's understanding of visualization artifacts. It is argued that design striving towards this ideal should be theoretically informed. The main problem of the thesis concerns to what extent the theory of embodied realism can serve as an informing theory for massification design. In order to investigate embodied realism as a candidate for informing massification design, two design projects are presented. Based on the design projects and associated evaluations, it is suggested that an embodied realist foundation for massification has the capacity to constrain and suggest form for expressions of abstract information. Suggestively, embodied realism may also inform design in such a way that it affects the experience of using the artifacts. The evaluations also suggest that design that draws on embodied meaning may come in conflict with conventional ways of expressing information. To further investigate a foundation for massification it is there is a need to investigate foundations that stays sensitive to conventional expressions. Additionally, it is suggested that massification design can be understood as striving towards authentic experiences of IT.

Wireless Sensor Networks (WSNs) are rapidly gaining popularity in various critical domains like health care, critical infrastructure, and climate monitoring, where application builders have diversified development needs. Independent of the functionalities provided by the WSN applications, many of the developers use visualization, simulation, and programming tools. However, these tools are designed as separate stand-alone applications, which force developers to use multiple tools. This situation often poses confusion and hampers an efficient development experience. To avoid the complexity of using multiple tools, a new, extensible, multi-platform, scalable, and open-source framework called PROVIZ is designed. PROVIZ is an integrated visualization and programming framework with the following features: PROVIZ 1) visualizes sensor nodes and WSN traffic by parsing the data received either from a packet sniffer (e.g., a sensor-based sniffer, or a commercial TI SmartRF 802.15.4 packet sniffer), or from a simulator (e.g., OMNeT); 2) visualizes a heterogeneous WSN consisting of different sensor nodes sending packets with different packet payload formats; and 3) provides a programming framework, which provides a graphical and script-based programming functionality, for developing WSN applications. Also, PROVIZ includes built-in extensible visual demo deployment capabilities that allow users to quickly craft network scenarios and share them with other users. Additionally, a secure and energy efficient wireless code dissemination protocol, named SIMAGE, was developed. SIMAGE is used by PROVIZ to wirelessly reprogram the sensor nodes. SIMAGE uses a link quality cognizant adaptive packet-sizing technique along with energy-efficient encryption protocols for secure and efficient code dissemination. In this thesis, the various features of PROVIZ's visualization and programming framework are explained, the functionality and performance of SIMAGE protocol is described, an example WSN security attack scenario is analyzed, and how PROVIZ can be used as a visual debugging tool to identify the security attack and aid in providing a software fix are discussed.

With the wide deployment of data intensive Internet applications and continued advances in sensing technology and biotechnology, large multidimensional datasets, possibly containing privacy-conscious information have been emerging. Mining such datasets has become increasingly common in business integration, large-scale scientific data analysis, and national security. The proposed research aims at exploring the geometric properties of the multidimensional datasets utilized in statistical learning and data mining, and providing novel techniques and frameworks for mining very large datasets while protecting the desired data privacy. The first main contribution of this research is the development of iVIBRATE interactive visualization-based approach for clustering very large datasets. The iVIBRATE framework uniquely addresses the challenges in handling irregularly shaped clusters, domain-specific cluster definition, and cluster-labeling of the data on disk. It consists of the VISTA visual cluster rendering subsystem, and the Adaptive ClusterMap Labeling subsystem. The second main contribution is the development of ``Best K Plot'(BKPlot) method for determining the critical clustering structures in multidimensional categorical data. The BKPlot method uniquely addresses two challenges in clustering categorical data: How to determine the number of clusters (the best K) and how to identify the existence of significant clustering structures. The method consists of the basic theory, the sample BKPlot theory for large datasets, and the testing method for identifying no-cluster datasets. The third main contribution of this research is the development of the theory of geometric data perturbation and its application in privacy-preserving data classification involving single party or multiparty collaboration. The key of geometric data perturbation is to find a good randomly generated rotation matrix and an appropriate noise component that provides satisfactory balance between privacy guarantee and data quality, considering possible inference attacks. When geometric perturbation is applied to collaborative multiparty data classification, it is challenging to unify the different geometric perturbations used by different parties. We study three protocols under the data-mining-service oriented framework for unifying the perturbations: 1) the threshold-satisfied voting protocol, 2) the space adaptation protocol, and 3) the space adaptation protocol with a trusted party. The tradeoffs between the privacy guarantee, the model accuracy and the cost are studied for the protocols.

The disciplines of informatics and information visualization have developed in response to societal needs to find new insight in complex datasets and have been enabled by technological advancements. Joint application of these fields can demonstrate themes and connections that are otherwise not apparent. Methodological approaches, such as direct network analysis, can be applied to policy documents to determine if action or policy recommendations match the goals or objectives stated in the within the same documents. Informatics and information visualization can also be used to analyze changes of themes found within the documents over time. This paper seeks to leverage informatics and information visualization methodologies as a novel approach to policy analysis. In particular, directed network and time burst techniques are used to analyze water management policy documents for the State of Texas. The congruency between the stated goals or objectives and recommendations sections is evaluated at a topical level within each planning document and possible changes in important water policy concepts over time are highlighted by comparing among multiple planning documents. Although there limitations to the process at the time of publication due to the newness of the software utilized, this paper demonstrates that the products still lead to unique and insightful conclusions. / text

Information technology has played several important roles in group decision making, such as communication support and decision support. Little is known about how information technology can be used to persuade members of a group to reach a consensus. In this dissertation, I aim to address the issues that are related to the role of visual representation technology (VRT) for persuasion in a forecasting context. VRTs are not traditional graphical representation technologies. VRTs can select, transform, and present data in a rich visual format that facilitates exploration, comprehension, and sense-making. The first study investigates conditions under which teams are likely to increase the use of VRTs and how the use of VRTs affects teams' consensus development and decision performance. The second study evaluates the effects of influence types and information technology on a choice shift. A choice shift is the tendency of group members to shift their initial positions to a more extreme direction following discussion. A choice shift is also called group polarization. To complement my first two studies, I conduct a laboratory experiment in my third study. I explore the effect of VRTs and team composition on a choice shift in group confidence.

With the increasing amount of collected data, large-scale high-dimensional data analysis is becoming essential in many areas. These data can be analyzed either by using fully computational methods or by leveraging human capabilities via interactive visualization. However, each method has its drawbacks. While a fully computational method can deal with large amounts of data, it lacks depth in its understanding of the data, which is critical to the analysis. With the interactive visualization method, the user can give a deeper insight on the data but suffers when large amounts of data need to be analyzed. Even with an apparent need for these two approaches to be integrated, little progress has been made. As ways to tackle this problem, computational methods have to be re-designed both theoretically and algorithmically, and the visual analytics system has to expose these computational methods to users so that they can choose the proper algorithms and settings. To achieve an appropriate integration between computational methods and visual analytics, the thesis focuses on essential computational methods for visualization, such as dimension reduction and clustering, and it presents fundamental development of computational methods as well as visual analytic systems involving newly developed methods. The contributions of the thesis include (1) the two-stage dimension reduction framework that better handles significant information loss in visualization of high-dimensional data, (2) efficient parametric updating of computational methods for fast and smooth user interactions, and (3) an iteration-wise integration framework of computational methods in real-time visual analytics. The latter parts of the thesis focus on the development of visual analytics systems involving the presented computational methods, such as (1) Testbed: an interactive visual testbed system for various dimension reduction and clustering methods, (2) iVisClassifier: an interactive visual classification system using supervised dimension reduction, and (3) VisIRR: an interactive visual information retrieval and recommender system for large-scale document data.

Networks are models that help us understanding and thinking about relationships between entities in the real world. Many of these networks are dynamic, i.e. connectivity changes over time. Understanding changes in connectivity means to understand interactions between elements of complex systems; how people create and break up friendship relations, how signals get passed in the brain, how business collaborations evolve, or how food-webs restructure after environmental changes. However, understanding static networks is already difficult, due to size, density, attributes and particular motifs; changes over time very much increase this complexity. Quantification of change is often insufficient, but beyond an analysis that is driven by technology and algorithms, humans dispose a unique capability of understanding and interpreting information in data, based on vision and cognition. This dissertation explores ways to interactively explore dynamic networks by means of visualization. I develop and evaluate techniques to unfold the complexity of dynamic networks, making them understandable by looking at them from different angles, decomposing them into their parts and relating the parts in novel ways. While most techniques for dynamic network visualization rely on one particular type of view on the data, complementary visualizations allow for higher-level exploration and analysis. Covering three aspects Tasks, Visualization Design and Evaluation, I develop and evaluate the following unfolding techniques: (i) temporal navigation between individual time steps of a network and improved animated transitions to better understand changes, (ii) designs for the comparison of weighted graphs, (iii) the Matrix Cube, a space-time cube based on adjacency matrices, allowing to visualize dense dynamic networks by, as well as GraphCuisine, a system to (iv) generate synthetic networks with the primary focus on evaluating visualizations in user studies. In order to inform the design and evaluation of visualizations, we (v) provide a task taxonomy capturing users' tasks when exploring dynamic networks. Finally, (vi) the idea of unfolding networks with Matrix Cubes is generalized to other data sets that can be represented in space-time cubes (videos, geographical data, etc.). Visualizations in these domains can inspire visualizations for dynamic networks, and vice-versa. We propose a taxonomy of operations, describing how 3D space-time cubes are decomposed into a large variety of 2D visualizations. These operations help us exploring the design space for visualizing and interactively unfolding dynamic networks and other spatio-temporal data, as well as may serve users as a mental model of the data.

Networks are models that help us understanding and thinking about relationships between entities in the real world. Many of these networks are dynamic, i.e. connectivity changes over time. Understanding changes in connectivity means to understand interactions between elements of complex systems; how people create and break up friendship relations, how signals get passed in the brain, how business collaborations evolve, or how food-webs restructure after environmental changes. However, understanding static networks is already difficult, due to size, density, attributes and particular motifs; changes over time very much increase this complexity. Quantification of change is often insufficient, but beyond an analysis that is driven by technology and algorithms, humans dispose a unique capability of understanding and interpreting information in data, based on vision and cognition. This dissertation explores ways to interactively explore dynamic networks by means of visualization. I develop and evaluate techniques to unfold the complexity of dynamic networks, making them understandable by looking at them from different angles, decomposing them into their parts and relating the parts in novel ways. While most techniques for dynamic network visualization rely on one particular type of view on the data, complementary visualizations allow for higher-level exploration and analysis. Covering three aspects Tasks, Visualization Design and Evaluation, I develop and evaluate the following unfolding techniques: (i) temporal navigation between individual time steps of a network and improved animated transitions to better understand changes, (ii) designs for the comparison of weighted graphs, (iii) the Matrix Cube, a space-time cube based on adjacency matrices, allowing to visualize dense dynamic networks by, as well as GraphCuisine, a system to (iv) generate synthetic networks with the primary focus on evaluating visualizations in user studies. In order to inform the design and evaluation of visualizations, we (v) provide a task taxonomy capturing users' tasks when exploring dynamic networks. Finally, (vi) the idea of unfolding networks with Matrix Cubes is generalized to other data sets that can be represented in space-time cubes (videos, geographical data, etc.). Visualizations in these domains can inspire visualizations for dynamic networks, and vice-versa. We propose a taxonomy of operations, describing how 3D space-time cubes are decomposed into a large variety of 2D visualizations. These operations help us exploring the design space for visualizing and interactively unfolding dynamic networks and other spatio-temporal data, as well as may serve users as a mental model of the data.

An Euler diagram C = {c_1, c_2,..., c_n} is a collection of n simple closed curves (i.e., Jordan curves) that partition the plane into connected subsets, called regions, each of which is enclosed by a unique combination of curves. Typically, Euler diagrams are used to visualize the distribution of discrete characteristics across a sample population; in this case, each curve represents a characteristic and each region represents the sub-population possessing exactly the combination of containing curves' properties. Venn diagrams are a subclass of Euler diagrams in which there are 2^n regions representing all possible combinations of curves (e.g., two partially overlapping circles). In this dissertation, we study the Euler Diagram Generation Problem (EDGP), which involves constructing an Euler diagram with a prescribed set of regions. We describe a graph-theoretic model of an Euler diagram's structure and use this model to develop necessary-and-sufficient existence conditions. We also use the graph-theoretic model to prove that the EDGP is NP-complete. In addition, we study the related Area-Proportional Euler Diagram Generation Problem (w-EDGP), which involves constructing an Euler diagram with a prescribed set of regions, each of which has a prescribed area. We develop algorithms for constructing area-proportional Euler diagrams composed of up to three circles and rectangles, as well as diagrams with an unbounded number of curves and a region of common intersection. Finally, we present implementations of our algorithms that allow the dynamic manipulation and real-time construction of area-proportional Euler diagrams.

This thesis examines techniques that provide a method of computer visualisation of bushfire spread. Existing techniques studied include, Kourtz & O???Regan, Green???s Contact, Heat Accumulation, Percolation modelling and Huygens??? Principle by Anderson et.al., French, Roberts, Richards. Many of these techniques are extended as part of a comprehensive study into how they perform in a two dimensional reference frame (ie over flat terrain only). New techniques are defined for Percolation Modelling and Huygens??? Principle. Each technique is examined in a series of test cases which include computer simulations with no wind, constant wind, variable wind, variable vegetation (including patchy fuel and two fuels) and where fuel burns out. These test cases provide: (a) an incremental approach to understanding the operation of each technique; (b) a basis for comparison; and (c) verification of correctness of the technique in two dimensions. Several of the techniques are shown, by these test cases, to be equivalent. For instance, the Kourtz & O???Regan technique using a square template is equivalent to the Contact Technique, Site percolation is similar to the Heat Accumulation technique and Template percolation is similar to the Contact Technique. Overall the Huygens??? Principle techniques provide the most accurate simulations of bushfire spread.