70,155 research outputs found

    FBA-SimVis: interactive visualization of constraint-based metabolic models

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    Summary: FBA-SimVis is a VANTED plug-in for the constraint-based analysis of metabolic models with special focus on the visual exploration of metabolic flux data resulting from model analysis. The program provides a user-friendly environment for model reconstruction, constraint-based model analysis, and interactive visualization of the simulation results. With the ability to quantitatively analyse metabolic fluxes in an interactive and visual manner, FBA-SimVis supports a comprehensive understanding of constraint-based metabolic flux models in both overview and detail

    Techniques and algorithms for immersive and interactive visualization of large datasets

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    Advances in computing power have made it possible for scientists to perform atomistic simulations of material systems that range in size, from a few hundred thousand atoms to one billion atoms. An immersive and interactive walkthrough of such datasets is an ideal method for exploring and understanding the complex material processes in these simulations. However rendering such large datasets at interactive frame rates is a major challenge. A scalable visualization platform is developed that is scalable and allows interactive exploration in an immersive, virtual environment. The system uses an octree based data management system that forms the core of the application. This reduces the amount of data sent to the pipeline without a per-atom analysis. Secondary algorithms and techniques such as modified occlusion culling, multiresolution rendering and distributed computing are employed to further speed up the rendering process. The resulting system is highly scalable and is capable of visualizing large molecular systems at interactive frame rates on dual processor SGI Onyx2 with an InfinteReality2 graphics pipeline

    Scalable visual analytics over voluminous spatiotemporal data

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    2018 Fall.Includes bibliographical references.Visualization is a critical part of modern data analytics. This is especially true of interactive and exploratory visual analytics, which encourages speedy discovery of trends, patterns, and connections in data by allowing analysts to rapidly change what data is displayed and how it is displayed. Unfortunately, the explosion of data production in recent years has led to problems of scale as storage, processing, querying, and visualization have struggled to keep pace with data volumes. Visualization of spatiotemporal data pose unique challenges, thanks in part to high-dimensionality in the input feature space, interactions between features, and the production of voluminous, high-resolution outputs. In this dissertation, we address challenges associated with supporting interactive, exploratory visualization of voluminous spatiotemporal datasets and underlying phenomena. This requires the visualization of millions of entities and changes to these entities as the spatiotemporal phenomena unfolds. The rendering and propagation of spatiotemporal phenomena must be both accurate and timely. Key contributions of this dissertation include: 1) the temporal and spatial coupling of spatially localized models to enable the visualization of phenomena at far greater geospatial scales; 2) the ability to directly compare and contrast diverging spatiotemporal outcomes that arise from multiple exploratory "what-if" queries; and 3) the computational framework required to support an interactive user experience in a heavily resource-constrained environment. We additionally provide support for collaborative and competitive exploration with multiple synchronized clients

    Finding faint HI structure in and around galaxies: scraping the barrel

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    Soon to be operational HI survey instruments such as APERTIF and ASKAP will produce large datasets. These surveys will provide information about the HI in and around hundreds of galaxies with a typical signal-to-noise ratio of \sim 10 in the inner regions and \sim 1 in the outer regions. In addition, such surveys will make it possible to probe faint HI structures, typically located in the vicinity of galaxies, such as extra-planar-gas, tails and filaments. These structures are crucial for understanding galaxy evolution, particularly when they are studied in relation to the local environment. Our aim is to find optimized kernels for the discovery of faint and morphologically complex HI structures. Therefore, using HI data from a variety of galaxies, we explore state-of-the-art filtering algorithms. We show that the intensity-driven gradient filter, due to its adaptive characteristics, is the optimal choice. In fact, this filter requires only minimal tuning of the input parameters to enhance the signal-to-noise ratio of faint components. In addition, it does not degrade the resolution of the high signal-to-noise component of a source. The filtering process must be fast and be embedded in an interactive visualization tool in order to support fast inspection of a large number of sources. To achieve such interactive exploration, we implemented a multi-core CPU (OpenMP) and a GPU (OpenGL) version of this filter in a 3D visualization environment (SlicerAstro\tt{SlicerAstro}).Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte

    GENE EXPRESSION PROSPECTIVE SIMULATION AND ANALYSIS USING DATA MINING AND IMMERSIVE VIRTUAL REALITY VISUALIZATION

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    Biological exploration on genetic expression and protein synthesis in living organisms is used to discover causal and interactive relationships in biological processes. Current GeneChip microarray technology provides a platform to an- alyze up to 500,000 molecular reactions on a single chip, providing thousands of genetic and protein expression results per test. Using visualization tools and priori knowledge of genetic and protein interactions, visual networks are used to model and analyze the results. The virtual reality environment designed and implemented for this project provides visualization and data modeling tools commonly used in genetic ex- pression data analysis. The software processes normalized genetic profile data from microarray testing results and association information from protein-to- protein databases. The data is modeled using a network of nodes to represent data points and edges to show relationships. This information is visualized in virtual reality and modeled using force directed networking algorithms in a fully explorable environment

    Exploring Coral Reefs with Interactive Geospatial Visualizations

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    This project uses geospatial data to generate custom polygons in an interactive setting to represent the size and location of coral reefs to extract insights from coral reef-centered data sets. Historically, the data used by the Reef Restoration Group Bonaire exists in disparate sources, making it difficult to track and analyze the outcomes of their restoration work. Additionally, this information is not available in a digestible format for other audiences who would be interested in this data, such as citizen scientists seeking coral reef health statistics, the general public wanting to better understand the coral reefs surrounding Bonaire or recreational scuba divers interested in learning more about potential dive sites. Numerous data points were extracted for each reef in scope, largely from two data sources to highlight the efforts of the Reef Restoration Group Bonaire and biodiversity of each reef. These data elements were visualized using Tableau, an interactive data visualization software, which provided the vehicle for the exploration and interaction with the data. The development of the custom Tableau interface and geospatial polygons representing the coral reefs, allowed for an interactive user experience for exploration and analysis of the health and biodiversity of each reef by plotting these polygons on a world map. The outcome gave the precise location and size for each reef allowing for the identification of reef boundaries using latitude and longitude coordinates as the polygon vertices. These outcomes indicate there is a tangible benefit possible by representing geospatial data in an interactive environment for data analysis and extraction of insights

    Exploranative Code Quality Documents

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    Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.Comment: IEEE VIS VAST 201

    JupyterLab_Voyager: A Data Visualization Enhancement in JupyterLab

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    With the emergence of big data, scientific data analysis and visualization (DAV) tools are critical components of the data science software ecosystem; the usability of these tools is becoming extremely important to facilitate next-generation scientific discoveries. JupyterLab has been considered as one of the best polyglot, web-based, open-source data science tools. As the next phase of extensible interface for the classic iPython Notebooks, this tool supports interactive data science and scientific computing across multiple programming languages with great performances. Despite these advantages, previous heuristics evaluation studies have shown that JupyterLab has some significant flaws in the data visualization side. The current DAV system in JupyterLab heavily relies on users’ understanding and familiarity with certain visualization libraries, and doesn’t support the golden visual-information-seeking mantra of “overview first, zoom and filter, then details-on-demand”. These limitations often lead to a workflow bottleneck at the start of a project. In this thesis, we present ‘JupyterLab_Voyager’, an extension for JupyterLab that provides a graphical user interface (GUI) for data visualization operations and couples faceted browsing with visualization recommendation to support exploration of multivariate, tabular data, as a solution to improve the usability of the DAV system. The new plugin works with various types of datasets in the JupyterLab ecosystem; using the plugin you can perform a high-level graphical analysis of fields within your dataset sans coding without leaving the JupyterLab environment. It helps analysts learn about the dataset and engage in both open-ended exploration and target specific answers from the dataset. User testings and evaluations demonstrated that this implementation has good usability and significantly improves the DAV system in JupyterLab

    Favecity: a visual exploration of city travel information

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    Web 2.0 provides a great environment for interactive information sharing and collaboration. We are no longer to receive information passively. On the contrary, everyone can contribute content and share personal experiences. It is a perfect social media for travelers to be connected. faveCITY is a visual exploration of what do people think and how do they feel about their favorite cities. It collects users\u27 opinions about the city such as city emotion, favorite city feature, best season to visit and a souvenir recommendation. The goal of this project is to combine Web 2.0 features with information visualization to create an information-sharing platform for city travelers. By visualizing these data, it could provide users unique travel advices, such as which city is the most popular travel destination right now, which city is the most romantic city etc. Furthermore, the platform is interactive and the data is live which means the results could be changed in real time
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