The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visualanalysis method for large-scale network exploration is inadequate. Efficient methods for visu...
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ISBN:
(纸本)9781467395915
The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visualanalysis method for large-scale network exploration is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: Enhanced Histogram Brush (EHB) and Flow-based Fast Newman (FFN) algorithm aim to assist the visualanalysis task in large-scale network exploration. The EHB is a novel improvement in Parallel Coordinates to guide exploratory interactions especially for big data. The FFN algorithm can efficiently discover the network hierarchy and extremely reduce the visual clutter in the network layout. A visualanalysis tool PCNET is designed and implemented on the basis of these two novel methods. PCNET is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of PCNET, we utilize the Chinavis2015 Challenge dataset as a case study.
datasets commonly include multi-value (set-typed) attributes that describe set memberships over elements, such as genres per movie or courses taken per student. Set-typed attributes describe rich relations across elem...
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datasets commonly include multi-value (set-typed) attributes that describe set memberships over elements, such as genres per movie or courses taken per student. Set-typed attributes describe rich relations across elements, sets, and the set intersections. Increasing the number of sets results in a combinatorial growth of relations and creates scalability challenges. Exploratory tasks (e.g. selection, comparison) have commonly been designed in separation for set-typed attributes, which reduces interface consistency. To improve on scalability and to support rich, contextual exploration of set-typed data, we present AggreSet. AggreSet creates aggregations for each data dimension: sets, set-degrees, set-pair intersections, and other attributes. It visualizes the element count per aggregate using a matrix plot for set-pair intersections, and histograms for set lists, set-degrees and other attributes. Its non-overlapping visual design is scalable to numerous and large sets. AggreSet supports selection, filtering, and comparison as core exploratory tasks. It allows analysis of set relations inluding subsets, disjoint sets and set intersection strength, and also features perceptual set ordering for detecting patterns in set matrices. Its interaction is designed for rich and rapid dataexploration. We demonstrate results on a wide range of datasets from different domains with varying characteristics, and report on expert reviews and a case study using student enrollment and degree data with assistant deans at a major public university.
Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its v...
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Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its visualanalysis. Current molecular visualization techniques utilize ambient occlusion as a global illumination approximation to improve spatial comprehension. Besides these shadow-like effects, interreflections are also known to improve the spatial comprehension of complex geometric structures. Unfortunately, the inherent computational complexity of interreflections would forbid interactive exploration, which is mandatory in many scenarios dealing with static and time-varying data. In this paper, we introduce a novel analytic approach for capturing interreflections of molecular structures in real-time. By exploiting the knowledge of the underlying space filling representations, we are able to reduce the required parameters and can thus apply symbolic regression to obtain an analytic expression for interreflections. We show how to obtain the data required for the symbolic regression analysis, and how to exploit our analytic solution to enhance interactive molecular visualizations.
This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analy...
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This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visualanalysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain.
The stress states encountered in ion implanted materials are often complicated, and, so analysis, and hence presentation thereof, is incomplete due to unavailability of computational and visual tools. Stress states de...
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The stress states encountered in ion implanted materials are often complicated, and, so analysis, and hence presentation thereof, is incomplete due to unavailability of computational and visual tools. Stress states determined by X-ray and neutron diffraction are usually calculated from the gradient of sin(2)Psi curves. This process, inter alia, contracts the stress to a scalar, losing the directionality and multivariate nature of the real stress state. In this paper we present a novel approach to the stress profile analysis and presentation obtained by X-ray diffraction. Stress is considered as a tensorial quantity from the initial measurement phase to the final presentation. This discussion inherently brings in the depth resolution of stresses, which in turn are explained by means of clear visual explanations. visual tools, which are not common use in the field, are introduced and the benefits as applied to ion implantation are discussed. (C) 2007 Elsevier B.v. All rights reserved.
Hierarchical clustering techniques complemented with visual display of data sets allow direct interpretation of the clustering results in terms of original variables. The proposed method of data ordering and display i...
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Hierarchical clustering techniques complemented with visual display of data sets allow direct interpretation of the clustering results in terms of original variables. The proposed method of data ordering and display is simple, informative and fulfils fundamental objectives of the datavisualisation techniques. In our study, it is applied for exploratory analysis of an environmental data set. (C) 2002 Elsevier Science B.v. All rights reserved.
It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial ...
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It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.
Recently proposed techniques have finally made it possible for analysts to interactively explore very large datasets in real time. However powerful, the class of analyses these systems enable is somewhat limited: spec...
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Recently proposed techniques have finally made it possible for analysts to interactively explore very large datasets in real time. However powerful, the class of analyses these systems enable is somewhat limited: specifically, one can only quickly obtain plots such as histograms and heatmaps. In this paper, we contribute Gaussian Cubes, which significantly improves on state-of-the-art systems by providing interactive modeling capabilities, which include but are not limited to linear least squares and principal components analysis (PCA). The fundamental insight in Gaussian Cubes is that instead of precomputing counts of many data subsets (as state-of-the-art systems do), Gaussian Cubes precomputes the best multivariate Gaussian for the respective data subsets. As an example, Gaussian Cubes can fit hundreds of models over millions of data points in well under a second, enabling novel types of visualexploration of such large datasets. We present three case studies that highlight the visualization and analysis capabilities in Gaussian Cubes, using earthquake safety simulations, astronomical catalogs, and transportation statistics. The dataset sizes range around one hundred million elements and 5 to 10 dimensions. We present extensive performance results, a discussion of the limitations in Gaussian Cubes, and future research directions.
Multidimensional visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed...
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Multidimensional visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visualexploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.
The Advanced volume visualization Display (AvvD) research program is a joint research program between the Fraunhofer Center for Research in Computer Graphics, Inc. and Innovative Research and Development Corp. It is d...
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ISBN:
(纸本)0819427381
The Advanced volume visualization Display (AvvD) research program is a joint research program between the Fraunhofer Center for Research in Computer Graphics, Inc. and Innovative Research and Development Corp. It is dedicated to the real-time visualization of high-resolution volumetric sensor data sets, maximizing the use of the human visual system to facilitate detection and classification in extremely hostile environments. The AvvD program has successfully demonstrated the application of high-speed volume visualization to a number of detection and classification problems. Recent emphasis has been on sonar for undersea imaging using data from the Naval Undersea Warfare Center - Division Newport's High Resolution Array (HRA), and rapid mine detection using data from the Coastal System Station's Toroidal volume Search Sonar (TvSS). The AvvD system introduced a new capability: the intuitive composition of several "pings" into a synthetic volumetric set. This composite data is higher resolution, approaching optical quality, with soft shadows and broad specularities.
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