Gunther H. Weber, Helwig Hauser, Gunther H. Weber et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2014
Abstract Interactive exploration and analysis of multi-field data utilizes a tight feed-back loop of computation/visualization and user interaction to facilitate knowledge discovery in complex datasets. It does so by providing both overview visualiza-tions, as well as support for focusing on features utilizing iterative drill-down oper-ations. When exploring multi-field data, interactive exploration and analysis relies on a combination of the following concepts: (i) physical views that show informa-tion in the context of the spatiotemporal domain (domain perspective), (ii) range views show relationships between multiple fields (range perspective), and (iii) se-lecting/marking data subsets in one view (e.g., regions in a physical view) leading to a consistent highlighting of this subset in all other views (brushing and linking). Based on these principles, interactive exploration and analysis supports building complex feature definitions, e.g., using Boolean operations to combine multiple se-lections. Utilizing derived fields, statistical methods, etc., adds a further layer of flexibility to this approach. Using these concepts, it is also possible to integrate fea-ture detection methods from the other chapters of this part, as well as application-specific feature extraction methods into an joint framework. This methodology of interactive visual data exploration and analysis has proven its potential in a larger number of successful applications. It has been implemented in a larger number of systems and is already available for a wide spectrum of different application do-mains.
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MOHAMMAD, ALHARBI · Cronfa (Swansea University) · 2021
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Mohammad Alharbi · 2021
This thesis is the result of a collaboration with the College of Arts and Humanities at Swansea University. The goal of this collaboration is to design novel visualization techniques to enable digital humanities scholars to explore and analyze parallel translations. To this end, chapter 2 introduces the first survey of surveys on text visualization which reviews all of the surveys and state-of-the-art reports on text visualization techniques, classifies them, provides recommendations, and discusses reported challenges.Following this, we present three visual interactive designs that support the typical digital humanities scholars workflow. In Chapter 4, we present VNLP, a visual, interactive design that enables users to explicitly observe the NLP pipeline processes and update the parameters at each processing stage. Chapter 5 presents AlignVis, a visual tool that provides a semi-automatic alignment framework to build a correspondence between multiple translations. It presents the results of using text similarity measurements and enables the user to create, verify, and edit alignments using a novel visual interface. Chapter 6 introduce TransVis, a novel visual design that supports comparison of multiple parallel translations. It incorporates customized mechanisms for rapid and interactive filtering and selection of a large number of German translations of Shakespeare’s Othello. All of the visual designs are evaluated using examples, detailed observations, case studies, and/or domain expert feedback from a specialist in modern and contemporary German literature and culture.Chapter 7 reports our collaborative experience and proposes a methodological workflow to guide such interdisciplinary research projects. This chapter also includes a summary of outcomes and lessons learned from our collaboration with the domain expert. Finally, Chapter 8 presents a summary of the thesis and future work directions.
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Chen, ningrui · DiVA (Linnaeus University) · 2021
Access to and understanding data plays an essential role in the increasingly digital world. Representation and analysis of relations between various data entities, i.e., graph and network structures in the data, is an important problem for various industries. In contrast to simple graphs that focus on edges with two endpoints only, a hypergraph provides a natural method to represent multi-way interactions with an arbitrary number of endpoints for each edge, and it can be a better alternative than a bipartite graph for comparable applications. However, traditional approaches for visually representing hypergraphs are purely static diagrams without support for interaction, which can be difficult to perceive and do not scale well with regard to the number of nodes and edges. They are not adequate for the representation and interactive exploration of large or dense hypergraph data sets found in real-world applications. The ISOVIS (Information and Software Visualisation) research group at Linnaeus University has previously introduced a novel radial visualization approach for undirected hypergraphs called Onion. The Onion tool focuses on solving the issues of edge clutter, overlaps, and edge crossings. However, certain open challenges and suggestions for improvements were identified for the respective implementation, and there is an opportunity to fill a gap in the hypergraph visualization research by building upon the original Onion approach study. In this thesis project, we implement the new version of the Onion approach based on the principles and challenges established previously. The contributions of this work include evidence regarding the effectiveness and efficiency of a hypergraph comparison technique, the usability of edge bundling in the context of hypergraph exploration tasks, and the scalability of the interactive visualization through an entirely new web-based version of the Onion approach. To obtain the respective results, the new implementation is applied for two case studies involving real-world data sets, and further validated through a user study with several participants. The results of this work can be helpful for researchers of network visualization and practitioners in need of approaches for representing and exploring data that can be modeled as hypergraphs.
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Tommy Dang · Figshare · 2014
Mining and visualizing huge image databases has become a daunting task for many application areas such as astronomy, medicine, geology, oceanography, and crime prevention. In this thesis, we introduce a new technique for feature extraction that obtains image signatures from pixels obtained by quantized images at different levels. We also present an application (PixSearcher) that retrieves and organizes huge images databases. In contrast to raster techniques that use the entire pixel raster for distance computations, our application uses a small set of descriptors to handle large image collections.\nOur research program began in 2010, when we were involved with characterizing the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. These characterizations included measures such as density, skewness, shape, outliers, and texture. Working directly with these measures, we located anomalous or interesting distributions in large collections of pairwise projections. For example, we used these measures to locate unusual correlations between pairs of time series and we used them to reorganize scatterplot matrices to avoid clutter.\nAlthough the original motivation was to characterize the 2D distributions of scatterplots, we soon realized the idea had more general implications. We extended this work to handle pixels in images and developed new descriptors that are appropriate for images. Using these descriptors led to algorithms that outperformed conventional techniques on speed and accuracy.
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Frits H. Post · Computer Graphics Forum · 2011
Abstract Data visualization is an application‐driven field, that is always trying to satisfy its customers and to adapt to the demands, cultures, and workflows of many application areas. Therefore, it is difficult to keep focus on techniques and approaches that are not too application specific. A lot of good work on data visualization consists of single‐problem solutions, that cannot be easily merged into general‐purpose systems. In this talk, I will briefly review some current trends and issues, and identify some approaches that are common to many applications. One such approach in data visualization that has attracted interest from the early days is the detection of salient features, or patterns of interest in a data set. The main idea is to extract information at a higher level of abstraction from a mass of data, that is richer in semantics but much smaller in size, and that can help to define scenes and objects for visualization. This idea was pioneered in areas such as flow visualization, but is now more widely applied. It is often considered to be necessity to keep up with the ever rapidly increasing size of data sets, and the demand for interactivity in data visualization and analysis. Another generic approach in data visualization is called interactive visual analysis (TVA), consisting of a strongly interactive multiple‐linked‐view interfaces with integrated, powerful data analysis techniques taken from statistical analysis, pattern recognition, machine learning, and other fields. This is built on the assumptions that a single 2D or 3D visualization is often not enough, and spatial views can be augmented with abstract, derived data spaces; that strong interaction helps to promote insight; and that a better balance is needed between human visual inspection and computer‐based analysis and reasoning. Interestingly, an IVA interface can serve not only as an environment for exploration of low‐level data, but also for defining the high‐level features to be extracted, that should summarize the essence of the data. The high‐level features are usually highly application specific, and can only be found using theories from the application domains. The big challenge is to create environments for general purpose visual data analysis, and yet allow users to introduce advanced theories and methods from many application domains. The trend towards more integration in data visualization will be illustrated with cross‐links between very different areas, such as medical and flow visualization, and the combined use of techniques from scientific visualization and information visualization, and the absorption of other data analysis techniques. Also, historic and contemporary examples of feature extraction and interactive visual analysis will be shown.
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Steffen Oeltze, Helmut Doleisch, Helwig Hauser et al. · IEEE Transactions on Visualization and Computer Graphics · 2007
Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimensionreduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.
Cited by 66
Florian Mansmann · 2015
The Internet has become a dangerous place: malicious code gets spread on personal comput-ers across the world, creating botnets ready to attack the network infrastructure at any time. Monitoring network traffic and keeping track of the vast number of security incidents or other anomalies in the network are challenging tasks. While monitoring and intrusion detection systems are widely used to collect operational data in real-time, attempts to manually analyze their output at a fine-granular level are often tedious, require exhaustive human resources, or completely fail to provide the necessary insight due to the complexity and the volume of the underlying data. This dissertation represents an effort to complement automatic monitoring and intrusion detection systems with visual exploration interfaces that empower human analysts to gain deeper insight into large, complex, and dynamically changing data sets. In this context, one key aspect of visual analysis is the refinement of existing visualization methods to improve their scalability with respect to a) data volume, b) visual limitations of computer screens, and c) human perception capacities. In addition to that, developmet of innovative visualization
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Association for Computational Linguistics 2021, Goel, Karan, Kryscinski, Wojciech et al. · Underline Science Inc. · 2021
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization. However, due to the black-box nature of neural models, uninformative evaluation metrics, and scarce tooling for model and data analysis, the true performance and failure modes of summarization models remain largely unknown. To address this limitation, we introduce SummVis, an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of the models, data, and evaluation metrics associated with text summarization. Through its lexical and semantic visualizations, the tools offers an easy entry point for in-depth model prediction exploration across important dimensions such as factual consistency or abstractiveness. The tool together with several pre-computed model outputs is available at https://summvis.com.
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Gutenko, Ievgeniia · Academic Commons (Stony Brook University) · 2017
With the size of volumetric medical data constantly growing, demand is increasing for efficient compression, storage, and understanding of data. Along with an increase in the size of information come two new major forms of data consumption. First of all, doctors no longer need to be tied to the hospital room to read through a single patient data but can do it in comfort of their home or on their mobile device. Secondly, widespread of browser-based tools enables new collaborative ways of information analysis and research. In this work, we focus on these two novel ways of analysis of medical information: a mobile device for single-subject data delivery and reading and browser-based collaborative research tools for visual analysis of large groups of subjects. Mobile devices have a number of unique characteristics that make them suitable platforms for mobile health applications. They are portable and can provide always-on connectivity with increasing throughput rates, effectively allowing a medical doctor to conduct the diagnostic process without being constrained to a static workstation. We construct and evaluate a dual pipeline for volumetric rendering on the mobile device and a remote rendering pipeline. Modern mobile devices also provide us new methods of input as an alternative to traditional mouse-keyboard interaction. We study two new ways of selection within volumetric data via new methods of input and construct respective models of human interaction. Next, we take a step further and evaluate novel mixed reality platform for simple immersive analysis tasks and interactions with volumetric data. With easier access to the data through protected hospital network, one can easily gather larger groups of subjects retrospectively for research in a specific problem domain. Often forgotten by laypeople, the spleen has risen as one of the examples of such problems. We develop a browser-based visual analytics application AnaFe to study changes in the spleen over time through multiple image-derived features. We take advantage of the amount of largely reproducible image features to enable construction of similarity-based queries through interaction with these features. Through rapid visual feedback, the user can compare several time-varying insights and their correlating features in a single overview. | 148 pages
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Carlos Scheidegger · 2016
In this chapter, we discuss interactive visualization of big data. We will talk about why this has recently become an active area of research and present some of the most promising recent work. We will discuss a broad range of ideas but will make no attempt at comprehensiveness; for that, we point readers to Wu et al.’s vision paper [25] and Godfrey et al.’s survey [9]. But before we get there, let us start with the basics: why should we care about visualization, and why should it be interactive?
Cited by 1
Taimur Khan · 2015
Maintaining complex software systems tends to be a costly activity where software engineers spend a significant amount of time trying to understand the system's structure and behavior. As early as the 1980s, operation and maintenance costs were already twice as expensive as the initial development costs incurred. Since then these costs have steadily increased. The focus of this thesis is to reduce these costs through novel interactive exploratory visualization concepts and to apply these modern techniques in the context of services offered by software quality analysis.
Costs associated with the understanding of software are governed by specific features of the system in terms of different domains, including re-engineering, maintenance, and evolution. These features are reflected in software measurements or inner qualities such as extensibility, reusability, modifiability, testability, compatability, or adatability. The presence or absence of these qualities determines how easily a software system can conform or be customized to meet new requirements. Consequently, the need arises to monitor and evaluate the qualitative state of a software system in terms of these qualities. Using metrics-based analysis, production costs and quality defects of the software can be recorded objectively and analyzed.
In practice, there exist a number of free and commercial tools that analyze the inner quality of a software system through the use of software metrics. However, most of these tools focus on software data mining and metrics (computational analysis) and only a few support visual analytical reasoning. Typically, computational analysis tools generate data and software visualization tools facilitate the exploration and explanation of this data through static or interactive visual representations. Tools that combine these two approaches focus only on well-known metrics and lack the ability to examine user defined metrics. Further, they are often confined to simple visualization methods and metaphors, including charts, histograms, scatter plots, and node-link diagrams.
The goal of this thesis is to develop methodologies that combine computational analysis methods together with sophisticated visualization methods and metaphors through an interactive visual analysis approach. This approach promotes an iterative knowledge discovery process through multiple views of the data where analysts select features of interest in one of the views and inspect data items of the select subset in all of the views. On the one hand, we introduce a novel approach for the visual analysis of software measurement data that captures complete facts of the system, employs a flow-based visual paradigm for the specification of software measurement queries, and presents measurement results through integrated software visualizations. This approach facilitates the on-demand computation of desired features and supports interactive knowledge discovery - the analyst can gain more insight into the data through activities that involve: building a mental model of the system; exploring expected and unexpected features and relations; and generating, verifying, or rejecting hypothesis with visual tools. On the other hand, we have also extended existing tools with additional views of the data for the presentation and interactive exploration of system artifacts and their inter-relations.
Contributions of this thesis have been integrated into two different prototype tools. First evaluations of these tools show that they can indeed improve the understanding of large and complex software systems.
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Marina Evers, Simon Leistikow, Hennes Rave et al. · IEEE Transactions on Visualization and Computer Graphics · 2024
Sensitivity analyses of simulation ensembles determine how simulation parameters influence the simulation's outcome. Commonly, one global numerical sensitivity value is computed per simulation parameter. However, when considering 3D spatial simulations, the analysis of localized sensitivities in different spatial regions is of importance in many applications. For analyzing the spatial variation of parameter sensitivity, one needs to compute a spatial sensitivity scalar field per simulation parameter. Given $n$n simulation parameters, we obtain multi-field data consisting of $n$n scalar fields when considering all simulation parameters. We propose an interactive visual analytics solution to analyze the multi-field sensitivity data. It supports the investigation of how strongly and in what way individual parameters influence the simulation outcome, in which spatial regions this is happening, and what the interplay of the simulation parameters is. Its central component is an overview visualization of all sensitivity fields that avoids 3D occlusions by linearizing the data using an adapted scheme of data-driven space-filling curves. The spatial sensitivity values are visualized in a combination of a Horizon Graph and a line chart. We validate our approach by applying it to synthetic and real-world ensemble data.
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Debra Hogue, Denielle Elliott, Chris Weaver · Lecture notes in computer science · 2025
Cited by 1
Margaret Varga, Adelica Ndoni, Susan Traeber-Burdin et al. · 2024
COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. It was first detected in China in December 2019. It spreads between people in close contact. On the 11<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> March 2020 the WHO declared the outbreak of the virus as a pandemic, which signaled a significant acceleration in the global response to the COVID-19 outbreak, and recognized the widespread transmission of the virus across multiple countries and continents. Data regarding COVID-19 was gathered and made available for open access. These data sources offer invaluable information for tracking, raising awareness and understanding of COVID-19, recognizing its impact, as well as informing the general public, health authorities, policy makers, situation managers and decision makers. However, COVID-19 data in its raw form is complex and difficult to understand and analyze. The application of visualization together with human factor design principles in a complex systems framework provides an effective means for exploiting these big and complex datasets. These techniques can transform such inherently non-visual data into intuitive visual forms that enable users to gain insight into, and understanding of, information contained within the data. This paper discusses the application of visualization and development of interactive dashboards, set in a complex systems framework, to provide an effective means for the users to explore, analyze and gain awareness of the situation, thus enabling informed decision making.
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Ove Daae Lampe · Bergen Open Research Archive (BORA) (University of Bergen) · 2011
Data gathered from processes, or process data, contains many different aspects that a visualization system should also convey. Aspects such as, temporal coherence, spatial connectivity, streaming data, and the need for in-situ visualizations, which all come with their independent challenges. Additionally, as sensors get more affordable, and the benefits of measurements get clearer we are faced with a deluge of data, of which sizes are rapidly growing. With all the aspects that should be supported and the vast increase in the amount of data, the traditional techniques of dashboards showing the recent data becomes insufficient for practical use. In this thesis we investigate how to extend the traditional process visualization techniques by bringing the streaming process data into an interactive visual analysis setting. The augmentation of process visualization with interactivity enables the users to go beyond the mere observation, pose questions about observed phenomena and delve into the data to mine for the answers. Furthermore, this thesis investigates how to utilize frequency based, as opposed to item based, techniques to show such large amounts of data. By utilizing Kernel Density Estimates (KDE) we show how the display of streaming data benefit by the non-parametric automatic aggregation to interpret incoming data put in context to historic data.
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Johannes Kehrer, Peter Filzmoser, Helwig Hauser · Computer Graphics Forum · 2010
Abstract We present a systematic study of opportunities for the interactive visual analysis of multi‐dimensional scientific data that is based on the integration of statistical aggregations along selected independent data dimensions in a framework of coordinated multiple views (with linking and brushing). Traditional and robust estimates of the four statistical moments (mean, variance, skewness, and kurtosis) as well as measures of outlyingness are integrated in an iterative visual analysis process. Brushing particular statistics, the analyst can investigate data characteristics such as trends and outliers. We present a categorization of beneficial combinations of attributes in 2D scatterplots: (a) k th vs. (k + 1) th statistical moment of a traditional or robust estimate, (b) traditional vs. robust version of the same moment, (c) two different robust estimates of the same moment. We propose selected view transformations to iteratively construct this multitude of informative views as well as to enhance the depiction of the statistical properties in scatterplots and quantile plots. In the framework, we interrelate the original distributional data and the aggregated statistics, which allows the analyst to work with both data representations simultaneously. We demonstrate our approach in the context of two visual analysis scenarios of multi‐run climate simulations.
Cited by 28Open access
Bernhard Preim · 2008
Abstract—Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimension reduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing. Index Terms—Multi-field Visualization, Visual Data Mining, Time-varying Volume Data, Integrating InfoVis/SciVis 1
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Cara L. Largesse, Patricia G. J. Twaalfhoven, Giampiero E. G. Beroggi et al. · 1995
Computerized policy analysis models have a long history of use. In most cases, however, they are designed to be used only by quantitative analysts, and their outputs are not directly understandable by policymakers and members of their staffs. Advances in informaton technology have resulted in work station models with interactive human-machine interfaces. This paper describes a decision support system, GO-FORWARD, that integrates visualization with a spreadsheet transportation policy analysis model developed by RAND/EAC for the Dutch Ministry of Transportation, Public Works and Water Management. Besides the technical aspects of the system (e.g., hypermedia and GIS capabilities), the paper also addresses the added value that it can provide for policy analysis and policymaking.
Cited by 1
Mikko Kuusela Räsänen, Raimo P. Hämäläinen, Jukka Ruusunen · 1994
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