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检索条件"任意字段=3rd Workshop on Topological Methods in Data Analysis and Visualization"

共 101 条记录，以下是51-60 订阅

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A Distributed-Memory Algorithm for Connected Components Labeling of Simulation data

A Distributed-Memory Algorithm for Connected Components Labe...

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workshop on the analysis of Large-scale High-dimensional and Multivariate data using Topology and Statistics

作者： Harrison, Cyrus Weiler, Jordan Bleile, Ryan Gaither, Kelly Childs, Hank Lawrence Livermore Natl Lab Livermore CA USA Univ Oregon Eugene OR 97403 USA Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9783662449004;9783662448991

This chapter describes a data-parallel, distributed-memory algorithm for identifying and labeling the connected sub-meshes within a three-dimensional mesh. The identification task is challenging in a distributed-memory setting because connectivity is transitive and the cells composing a sub-mesh may span many processors. The algorithm employs a multi-stage application of the Union-find algorithm and a spatial partitioning scheme to efficiently merge information across processors and to produce a global labeling of connected sub-meshes. Marking each vertex with its corresponding sub-mesh label allows mesh features to be isolated based on topology, enabling important analysis capabilities. The algorithm performs well in parallel;results are presented from a weak scaling study with concurrency levels up to 2,197 cores and meshes containing over two billion cells. This chapter is an extension of previous work by Harrison et al. (data-parallel mesh connected components labeling and analysis. In: EuroGraphics Symposium on Parallel Graphics and visualization (EGPGV), pp. 131–140, April 2011). It contains significant algorithmic improvements over the previous version, improved exploration of key bottlenecks in the algorithm, and improved clarity of presentation. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Mesh generation

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Visual analytics of software structure and metrics

Visual analytics of software structure and metrics

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IEEE International workshop on Visualizing Software for Understanding and analysis (VISSOFT)

作者： Taimur Khan Henning Barthel Achim Ebert Peter Liggesmeyer University of Kaiserslautern Kaiserslautern Germany Fraunhofer IESE Kaiserslautern Germany

In terms of software maintenance and comprehension, the fields of software engineering and software visualization have produced several methods and tools. However, they are typically separate tools in practice. In this paper, we present a novel methodology of combining software analysis and software visualization tools via an interactive visual workflow modeling approach. Standard software analysis tools are also limited in that they support only well-known metrics or are too complicated to use for generating custom software metrics. To address these shortcomings, our approach focuses on visual elements, their configurations, and interconnectivity rather than a data ontology and querying language. In order to test and validate our methodology, we developed a prototype tool called VIMETRIK (Visual Specification of Metrics). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regard to understanding software measurement and analysis data.

关键词： Software data visualization Analytical models Object oriented modeling visualization Software measurement

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Unified model for software engineering data

Unified model for software engineering data

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IEEE International workshop on Visualizing Software for Understanding and analysis (VISSOFT)

作者： Anna-Liisa Mattila Antti Luoto Henri Terho Otto Hylli Outi Sievi-Korte Kari Systä Department of Pervasive Computing Tampere University of Technology Tampere Finland

Software process data is available in several tools such as version control systems, issue trackers and test and build systems to name a few. Using the data gathered in these software engineering tools would be ideal for collecting different kinds of software processes and product metrics as the data is already automatically gathered by the tools. However, the tools present and store the data in various formats. The data collection methods and interfaces also vary between the tools. This closes the software engineering data into silos and makes it hard to build reusable analysis and visualizations for the data. In this position paper we present a unified model for software engineering data and a framework for data collection, conversion and storing that utilizes our model. The aim of the model is to define a common format for software engineering data which is not dependent on specific software engineering tools or the software engineering process and thus can be used as a basis for building reusable visualization and analysis components. To demonstrate that we can build reusable visualization plugins on top of the framework, we created a timeline visualization plugin. The visualization plugin is used to visualize two data sets from industrial software projects that have different contexts and semantics.

关键词： data visualization Software engineering data models Software data collection Joining processes Control systems

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Towards effective clustering techniques for the analysis of electric power grids 13

Towards effective clustering techniques for the analysis of ...

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3rd Int'l workshop on High Performance Computing, Networking and Analytics for the Power Grid, HiPCNA-PG 2013 - Held in Conjunction with the International Conference for High Performance Computing, Networking, Storage and analysis, SC 2013

作者： Hogan, Emilie Cotilla-Sanchez, Eduardo Halappanavar, Mahantesh Wang, Shaobu Mackey, Patrick Hines, Paul Huang, Zhenyu Pacific Northwest National Laboratory United States Oregon State University United States University of Vermont United States

ISBN: (纸本)9781450325103

Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems. © 2013 ACM.

关键词： Phasor measurement units

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Hypothesis generation by interactive visual exploration of heterogeneous medical data

Hypothesis generation by interactive visual exploration of h...

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3rd International workshop on Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big data, HCI-KDD 2013, Held at SouthCHI 2013

作者： Turkay, Cagatay Lundervold, Arvid Lundervold, Astri Johansen Hauser, Helwig Department of Informatics University of Bergen Norway Department of Biomedicine University of Bergen Norway Department of Biological and Medical Psychology University of Bergen Norway

ISBN: (纸本)9783642391453

High dimensional, heterogeneous datasets are challenging for domain experts to analyze. A very large number of dimensions often pose problems when visual and computational analysis tools are considered. Analysts tend to limit their attention to subsets of the data and lose potential insight in relation to the rest of the data. Generating new hypotheses is becoming problematic due to these limitations. In this paper, we discuss how interactive analysis methods can help analysts to cope with these challenges and aid them in building new hypotheses. Here, we report on the details of an analysis of data recorded in a comprehensive study of cognitive aging. We performed the analysis as a team of visualization researchers and domain experts. We discuss a number of lessons learned related to the usefulness of interactive methods in generating hypotheses. © 2013 Springer-Verlag.

关键词： visualization

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Improving 3D spatial queries search: Newfangled technique of space filling curves in 3D city modeling 8

Improving 3D spatial queries search: Newfangled technique of...

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ISPRS 8th 3D GeoInfo Conference and WG II/2 workshop

作者： Uznir, U. Anton, F. Suhaibah, A. Rahman, A.A. Mioc, D. Dept. of Geoinformation Faculty of Geoinformation and Real Estate Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Dept. of Geodesy National Space Institute Denmark Technical University Lyngby2800 Denmark

The advantages of three dimensional (3D) city models can be seen in various applications including photogrammetry, urban and regional planning, computer games, etc.. They expand the visualization and analysis capabilities of Geographic Information Systems on cities, and they can be developed using web standards. However, these 3D city models consume much more storage compared to two dimensional (2D) spatial data. They involve extra geometrical and topological information together with semantic data. Without a proper spatial data clustering method and its corresponding spatial data access method, retrieving portions of and especially searching these 3D city models, will not be done optimally. Even though current developments are based on an open data model allotted by the Open Geospatial Consortium (OGC) called CityGML, its XML-based structure makes it challenging to cluster the 3D urban objects. In this research, we propose an opponent data constellation technique of space-filling curves (3D Hilbert curves) for 3D city model data representation. Unlike previous methods, that try to project 3D or n-dimensional data down to 2D or 3D using Principal Component analysis (PCA) or Hilbert mappings, in this research, we extend the Hilbert space-filling curve to one higher dimension for 3D city model data implementations. The query performance was tested using a CityGML dataset of 1,000 building blocks and the results are presented in this paper. The advantages of implementing space-filling curves in 3D city modeling will improve data retrieval time by means of optimized 3D adjacency, nearest neighbor information and 3D indexing. The Hilbert mapping, which maps a sub-interval of the [0, 1] interval to the corresponding portion of the d-dimensional Hilbert's curve, preserves the Lebesgue measure and is Lipschitz continuous. Depending on the applications, several alternatives are possible in order to cluster spatial data together in the third dimension compared to its clusteri

关键词： 3D Adjacency 3D City Modeling 3D Indexing 3D Space Filling Curves 3D Spatial data Management

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Evaluation of Spatial Relations in watermarked Geospatial data

Evaluation of Spatial Relations in watermarked Geospatial Da...

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3rd ACM SIGSPATIAL International workshop on GeoStreaming (IWGS)

作者： Zope-Chaudhari, Sangita Venkatachalam, P. Indian Inst Technol Ctr Studies Resources Engn Bombay Maharashtra India

ISBN: (纸本)9781450316958

Due to rapid growth of distributed network and Internet, it becomes easy for data providers and users to access, manage, and share voluminous geospatial data in digital form through geostreaming methods. The increased availability of tools and techniques to access and disseminate geospatial data has made more urgent to protect it. In recent years, there is significant research on copyright protection of geospatial vector data using digital watermarking. As geospatial data is different from digital images, image watermarking evaluation methods cannot be directly applied to watermarked geospatial data. At present, watermarking algorithms are mainly focusing on robustness evaluation and error analysis. One of the important aspects related to vector data quality i. e. topological relationship inspection is neglected. In this paper, an attempt has been made to incorporate invisible watermark in geospatial vector data using wavelet based watermarking algorithm and the resulted watermarked data has been evaluated in terms of polygon closure, topological relationship consistency and impact on visualization. The study helps to choose suitable watermark embedding strength such that the topological relationship can be preserved in addition to robustness.

关键词： Geospatial data data quality spatial relations watermark

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topological methods in data analysis and visualization II 2012

引用

丛书名： Mathematics and visualization

2012年

作者： Ronald Peikert Helwig Hauser Hamish Carr Raphael Fuchs

ISBN: (数字)9783642231759

ISBN: (纸本)9783642231742

When scientists analyze datasets in a search for underlying phenomena, patterns or causal factors, their first step is often an automatic or semi-automatic search for structures in the data. Of these feature-extraction methods, topological ones stand out due to their solid mathematical foundation. topologically defined structuresas found in scalar, vector and tensor fieldshave proven their merit in a wide range of scientific domains, and scientists have found them to be revealing in subjects such as physics, engineering, and medicine. Full of state-of-the-art research and contemporary hot topics in the subject, this volume is a selection of peer-reviewed papers originally presented at the fourth workshop on Topology-Based methods in data analysis and visualization, TopoInVis 2011, held in Zurich, Switzerland. The workshop brought together many of the leading lights in the field for a mixture of formal presentations and discussion. One topic currently generating a great deal of interest, and explored in several chapters here, is the search for topological structures in time-dependent flows, and their relationship with Lagrangian coherent structures. Contributors also focus on discrete topologies of scalar and vector fields, and on persistence-based simplification, among other issues of note. The new research results included in this volume relate to all three key areas in data analysistheory, algorithms and applications.

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visualization and exploration of 3D toponome data

Visualization and exploration of 3D toponome data

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3rd Eurographics workshop on VisualComputing in Biology and Medicine, EG VCBM 2012

作者： Oeltze, S. Klemm, P. Hillert, R. Preim, B. Schubert, W. Department of Simulation and Graphics University of Magdeburg Germany Molecular Pattern Recognition Research Group University of Magdeburg Germany

ISBN: (纸本)9783905674385

The toponome of a cell describes the location and topological distribution of proteins across the cell. In toponomics, the toponome is imaged and its inner structure and its semantics are investigated in order to understand how cells encode different functionalities both in health and disease. Toponome imaging results in complex multiparameter data composed of a 3D volume per protein affinity reagent. After imaging, the data is binarized such that 1 encodes protein present and 0 encodes protein absent. Biologists are particularly interested in the clustering of these binary protein patterns and in the distribution of clusters across the cell. We present a volume rendering approach for visualizing all unique protein patterns in 3D. A unique color is dynamically assigned to each pattern such that a sufficient perceptual difference between colors in the current view is guaranteed. We further present techniques for interacting with the view in an exploratory analysis. The biologist may for instance "peel of" clusters thereby revealing occluded cell structures. The 3D view is integrated in a multiple coordinated view system. Peeling off clusters or brushing protein patterns in the view updates all other views. We demonstrate the utility of the view with a cell sample containing lymphocytes. © The Eurographics Association 2012.

关键词： Proteins

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4th workshop on Topology Based methods in data analysis and visualization, TopoInVis 2011

4th Workshop on Topology Based Methods in Data Analysis and ...

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4th workshop on Topology Based methods in data analysis and visualization, TopoInVis 2011

ISBN: (纸本)9783642543005

The proceedings contain 19 papers. The special focus in this conference is on Topology Based methods in data analysis and visualization. The topics include: Glyphs for non-linear vector field singularities;2D asymmetric tensor field topology;on the elusive concept of Lagrangian coherent structures;ridge concepts for the visualization of Lagrangian coherent structures;Filtering of FTLE for visualizing spatial separation in unsteady 3D flow;A variance based FTLE-like method for unsteady uncertain vector fields;on the finite-time scope for computing lagrangian coherent structures from Lyapunov exponents;Scale-space approaches to FTLE ridges;efficient computation of a hierarchy of discrete 3D gradient vector fields;computing simply-connected cells in three-dimensional Morse-Smale complexes;combinatorial vector field topology in three dimensions;topological cacti: Visualizing contour-based statistics;enhanced topology-sensitive clustering by reeb graph shattering;efficient computation of persistent homology for cubical data;visualizing invariant manifolds in area-preserving maps;understanding quasi-periodic fieldlines and their topology in toroidal magnetic fields;preface.

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