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interactive visual analysis on the Attack and Defense Drill of Grid Cyber-physical Systems

CSEE Journal of Power and Energy Systems 2021年第1期7卷 45-56页

作者： Kehe Wu Jiawei Li Yayun Zhu Siwei Miao Sixun Zhu Chunjie Zhou North China Electric Power University Beijing102206China China Electric Power Research Institute Beijing100031China Huazhong University of Science and Technology Wuhan430074China

The open and distributed connection of the powersystem makes it vulnerable to various potential cyber-attacks,which may lead to power outages and even casualties. Therefore,the construction of attack and defense drill (ADD) platforms forattack mechanism investigation and protection strategy evaluationhas become a research hotspot. However, for the massiveand heterogeneous security analysis data generated during thedrill, it is rare to have a comprehensive and intuitive methodto visually and efficiently display the perspective of the attackerand defender. In order to solve this problem, this paper proposesa visual analysis scheme of an ADD framework for a grid cyberphysicalsystem (GCPS) based on the interactive visual analysismethod. Specifically, it realizes system weakness discovery basedon knowledge visualization, optimization of the detection modeland visualization interaction. Finally, the case study on thesimulation platform of ADD proves the effectiveness of theproposed method.

关键词： Attack and defense drill(ADD) attack path interactive visual analysis intrusion detection

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interactive visual analysis of contrast-enhanced ultrasound data based on small neighborhood statistics

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COMPUTERS & GRAPHICS-UK 2011年第2期35卷 218-226页

作者： Angelelli, Paolo Nylund, Kim Gilja, Odd Helge Hauser, Helwig Univ Bergen Dept Informat N-5020 Bergen Norway Univ Bergen Inst Med N-5020 Bergen Norway Haukeland Hosp Natl Ctr Gastroenterol Ultrasonog N-5021 Bergen Norway

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization in cancer diagnosis. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper we present a pipeline that enables interactive visual exploration and semi-automatic segmentation and classification of CEUS data. For the visual analysis of this challenging data, with characteristic noise patterns and residual movements, we propose a robust method to derive expressive enhancement measures from small spatio-temporal neighborhoods. We use this information in a staged visual analysis pipeline that leads from a more local investigation to global results such as the delineation of anatomic regions according to their perfusion properties. To make the visual exploration interactive, we have developed an accelerated framework based on the OpenCL library, that exploits modern many-cores hardware. Using our application, we were able to analyze datasets from CEUS liver examinations, being able to identify several focal liver lesions, segment and analyze them quickly and precisely, and eventually characterize them. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Medical visualization interactive visual analysis Contrast-enhanced ultrasound

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interactive visual analysis of Heterogeneous Scientific Data across an Interface

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第7期17卷 934-946页

作者： Kehrer, Johannes Muigg, Philipp Doleisch, Helmut Hauser, Helwig Univ Bergen Dept Informat N-5020 Bergen Norway Vienna Univ Technol Inst Comp Graph & Algorithms A-1040 Vienna Austria SimVis GmbH A-1220 Vienna Austria

We present a systematic approach to the interactive visual analysis of heterogeneous scientific data. The data consist of two interrelated parts given on spatial grids over time (e.g., atmosphere and ocean part from a coupled climate model). By integrating both data parts in a framework of coordinated multiple views (with linking and brushing), the joint investigation of features across the data parts is enabled. An interface is constructed between the data parts that specifies 1) which grid cells in one part are related to grid cells in the other part, and vice versa, 2) how selections (in terms of feature extraction via brushing) are transferred between the two parts, and 3) how an update mechanism keeps the feature specification in both data parts consistent during the analysis. We also propose strategies for visual analysis that result in an iterative refinement of features specified across both data parts. Our approach is demonstrated in the context of a complex simulation of fluid-structure interaction and a multirun climate simulation.

关键词： interactive visual analysis heterogeneous scientific data coordinated multiple views

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interactive visual analysis and classification of hyperspectral imaging data

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JOURNAL OF COMPUTATIONAL SCIENCE 2018年 26卷 13-21页

作者： Antonov, Anatoliy Linsen, Lars Jacobs Univ Dept Comp Sci & Elect Engn Earth Syst Sci Res Sch ESSReS Bremen Germany Jacobs Univ Computat Sci & Comp Sci Dept Comp Sci & Elect Engn Bremen Germany Westfalische Wilhelms Univ Munster Comp Sci Inst Comp Sci Munster Germany

Hyperspectral imaging is a widely used remote sensing technique in planetary sciences. Captured data consist of arrays of images of the same scene taken at a high number of sensor wavelengths. Studying these data, scientists search to understand, for example, the mineral composition of the surface, or types and kinds of vegetation present in the region. Different mineral or vegetation classes induce spectral features that allow for their identification and mapping. The two main tasks are selecting a subset of bands that best capture the relevant spectral features and separating different mixtures from summary products in these bands. It is common practice that the subsets of bands used in the analysis are predefined based on experience, i.e., without taking into account actual data. Then, classification of the regions is performed by analyzing the summary products. We, instead, propose an approach that allows for data-driven selection of subsets of bands and for a more accurate separation of different mixtures. Our approach relies on an interactive visual analysis using suitable visual encodings and interaction mechanisms. In a first step, we produce a similarity plot of the bands of the hyperspectral imaging data by employing a projection-based dimensionality reduction technique. The similarity plot allows for the selection of most informative bands. In a second step, we apply an automatic hierarchical density-based clustering approach to the pixels of the selected bands. The resulting cluster hierarchy is interactively explored and adjusted using coordinated views of a cluster tree visualization, a parallel coordinate plot of the bands, and a spatial data visualization. Brushing and linking in the coordinated views allows for an intuitive interactive analysis of the bands leading to the desired mineral mapping result. The effectiveness of our approach is demonstrated by applying it to mineral mapping of Compact Reconnaissance Imaging Spectrometer for Mars data and

关键词： interactive visual analysis Hyperspectral imagery Data reduction Clustering

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interactive visual analysis of Set-Typed Data

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2008年第6期14卷 1340-1347页

作者： Freiler, Wolfgang Matkovic, Kresimir Hauser, Helwig VRVis Res Ctr Vienna Austria Univ Bergen N-5020 Bergen Norway

While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item - like one cell in a table - has a list of n >= 0 elements as its value. We present the set'o'gram as a new visualization approach to represent data of set type and to enable interactive visual exploration and analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have a larger number of dimensions (more than a dozen or more), especially also in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people.

关键词： interactive visual analysis Multidimensional Multivariate Data visualization Categorical Data visualization interactive visualization Focus plus Context visualization Multiple Coordinated Views

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interactive visual analysis AS A SUPPORT OF OPTIMIZATION AND analysis OF INTERNAL COMBUSTION ENGINES

INTERACTIVE VISUAL ANALYSIS AS A SUPPORT OF OPTIMIZATION AND...

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11th International Design Conference (DISIGN)

作者： Matkovic, K. Klarin, B. Jelovic, M. Duras, M. VRVis Res Ctr Vienna Austria

ISBN: (纸本)9789537738037

Multiple simulation runs, a process where many simulations with different parameters of the same model are ran, helps engine designers in getting insight. In order to cope with increased complexity and amount of results advanced techniques are needed. interactive visual analysis can support analysis of such a data. Three projects: rapid prototyping of a common rail injection system, analysis of a timing chain drive, and analysis of an elastohydrodynamic lubrication bearing are described. All of these projects result from a cooperation between visualization experts and mechanical engineers.

关键词： interactive visual analysis common rail injection chain drive EHD bearing

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interactive visual analysis of Complex Scientific Data as Families of Data Surfaces

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 1351-1358页

作者： Matkovic, Kresimir Gracanin, Denis Klarin, Borislav Hauser, Helwig VRVis Res Ctr Vienna Austria Virginia Tech Blacksburg VA 24061 USA AVL AST DOO Zagreb Croatia Univ Bergen N-5020 Bergen Norway

The widespread use of computational simulation in science and engineering provides challenging research opportunities. Multiple independent variables are considered and large and complex data are computed, especially in the case of multi-run simulation. Classical visualization techniques deal well with 2D or 3D data and also with time-dependent data. Additional independent dimensions, however, provide interesting new challenges. We present an advanced visual analysis approach that enables a thorough investigation of families of data surfaces, i.e., datasets, with respect to pairs of independent dimensions. While it is almost trivial to visualize one such data surface, the visual exploration and analysis of many such data surfaces is a grand challenge, stressing the users' perception and cognition. We propose an approach that integrates projections and aggregations of the data surfaces at different levels (one scalar aggregate per surface, a 1 D profile per surface, or the surface as such). We demonstrate the necessity for a flexible visual analysis system that integrates many different (linked) views for making sense of this highly complex data. To demonstrate its usefulness, we exemplify our approach in the context of a meteorological multi-run simulation data case and in the context of the engineering domain, where our collaborators are working with the simulation of elastohydrodynamic (EHD) lubrication bearing in the automotive industry.

关键词： interactive visual analysis family of surfaces coordinated multiple views multidimensional multivariate data

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interactive visual analysis of Complex Scientific Data as Families of Data Surfaces

Interactive Visual Analysis of Complex Scientific Data as Fa...

引用

IEEE Information visualization Conference/IEEE visualization Conference

关键词： interactive visual analysis family of surfaces coordinated multiple views multidimensional multivariate data

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Representative Factor Generation for the interactive visual analysis of High-Dimensional Data

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2012年第12期18卷 2621-2630页

作者： Turkay, Cagatay Lundervold, Arvid Lundervold, Astri Johansen Hauser, Helwig Univ Bergen Dept Informat N-5020 Bergen Norway Univ Bergen Dept Biomed N-5020 Bergen Norway Univ Bergen Dept Biol & Med Psychol N-5020 Bergen Norway

Datasets with a large number of dimensions per data item (hundreds or more) are challenging both for computational and visual analysis. Moreover, these dimensions have different characteristics and relations that result in sub-groups and/or hierarchies over the set of dimensions. Such structures lead to heterogeneity within the dimensions. Although the consideration of these structures is crucial for the analysis, most of the available analysis methods discard the heterogeneous relations among the dimensions. In this paper, we introduce the construction and utilization of representative factors for the interactive visual analysis of structures in high-dimensional datasets. First, we present a selection of methods to investigate the sub-groups in the dimension set and associate representative factors with those groups of dimensions. Second, we introduce how these factors are included in the interactive visual analysis cycle together with the original dimensions. We then provide the steps of an analytical procedure that iteratively analyzes the datasets through the use of representative factors. We discuss how our methods improve the reliability and interpretability of the analysis process by enabling more informed selections of computational tools. Finally, we demonstrate our techniques on the analysis of brain imaging study results that are performed over a large group of subjects.

关键词： interactive visual analysis high-dimensional data analysis

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Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets

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visual COMPUTER 2018年第6-8期34卷 1087-1098页

作者： Splechtna, Rainer Beham, Michael Gracanin, Denis Lujan Ganuza, Maria Buehler, Katja Pandzic, Igor Sunday Matkovic, Kresimir VRVis Res Ctr Donau City Str 11 A-1220 Vienna Austria Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA UNS DCIC VyGLab Res Lab San Andres 800 RA-8000 Bahia Blanca Buenos Aires Argentina Univ Zagreb Fac Elect Engn & Comp Dept Telecommun Unska 3 Zagreb 10000 Croatia

Studying complex problems often requires identifying and exploring connections and dependencies among several, seemingly unrelated, data sets. Those data sets are often represented as data tables. We propose a novel approach to studying such data sets using linking and brushing across multiple data tables in a coordinated multiple views system. We first identify possible mappings from a subset of one data set to a subset of another data set. That collection of mappings is then used to specify linking among data sets and to support brushing across data sets. Brushing in one data set is then mapped to a brush in the destination data set. If the brush is refined in the destination data set, the inverse mapping, or a back-link, is used to determine the refined brush in the original data set. Brushing and back-links make it possible to efficiently create and analyze complex queries interactively in an iterative process. That process is further supported by a user interface that keeps track of the mappings, links and brushes. The proposed approach is evaluated using three data sets.

关键词： visual analytics interactive visual analysis Multiple data sets analysis

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