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ActiviTree: Interactive visual exploration of Sequences in Event-Based data Using Graph Similarity

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 945-952页

作者： Vrotsou, Katerina Johansson, Jimmy Cooper, Matthew Linkoping Univ S-58183 Linkoping Sweden

The identification of significant sequences in large and complex event-based temporal data is a challenging problem with applications in many areas of today's information intensive society. Pure visual representations can be used for the analysis, but are constrained to small data sets. Algorithmic search mechanisms used for larger data sets become expensive as the data size increases and typically focus on frequency of occurrence to reduce the computational complexity, often overlooking important infrequent sequences and outliers. In this paper we introduce an interactive visual data mining approach based on an adaptation of techniques developed for web searching, combined with an intuitive visual interface, to facilitate user-centred exploration of the data and identification of sequences significant to that user. The search algorithm used in the exploration executes in negligible time, even for large data, and so no pre-processing of the selected data is required, making this a completely interactive experience for the user. Our particular application area is social science diary data but the technique is applicable across many other disciplines.

关键词： Interactive visual exploration event-based data sequence identification graph similarity node similarity

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visual exploration of Nasal Airflow

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 1407-1414页

作者： Zachow, Stefan Muigg, Philipp Hildebrandt, Thomas Doleisch, Helmut Hege, Hans-Christian ZIB Berlin Germany Vienna Univ Technol Inst Comp Graph & Algorithms Vienna Austria

Rhinologists are often faced with the challenge of assessing nasal breathing from a functional point of view to derive effective therapeutic interventions. While the complex nasal anatomy can be revealed by visual inspection and medical imaging, only vague information is available regarding the nasal airflow itself: Rhinomanometry delivers rather unspecific integral information on the pressure gradient as well as on total flow and nasal flow resistance. In this article we demonstrate how the understanding of physiological nasal breathing can be improved by simulating and visually analyzing nasal airflow, based on an anatomically correct model of the upper human respiratory tract. In particular we demonstrate how various Information visualization (InfoVis) techniques, such as a highly scalable implementation of parallel coordinates, time series visualizations, as well as unstructured grid multi-volume rendering, all integrated within a multiple linked views framework, can be utilized to gain a deeper understanding of nasal breathing. Evaluation is accomplished by visual exploration of spatio-temporal airflow characteristics that include not only information on flow features but also on accompanying quantities such as temperature and humidity. To our knowledge, this is the first in-depth visual exploration of the physiological function of the nose over several simulated breathing cycles under consideration of a complete model of the nasal airways, realistic boundary conditions, and all physically relevant time-varying quantities.

关键词： Flow visualization exploratory data analysis interactive visual analysis of scientific data time-dependent data

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Towards a Highly Interactive Web exploration of Spatial and Historical Aggregations from data Marts

Towards a Highly Interactive Web Exploration of Spatial and ...

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International conference on Advanced Geographic Information Systems and Web Services

作者： Silva, Sonia Fernandes Etruria Telemat Srl Siena Italy

ISBN: (纸本)9781424433636

With the recent advances in the area of WebGIS and Spatial OLAP, new approaches include geographical display and navigation during the explorative analysis of multidimensional data. Such geographical displays can be enriched with visual diagrams for effective data exploration for decision-making. Within this context, we developed a web tool which provides visual interaction with geo-referenced multidimensional data. The implementation of such tool is based on a new approach that puts together some existing techniques in the literature for data exploration and optimization. As a consequence, the tool enables the end-user to remotely create and explore several interactive visual reports of summarized data almost instantaneously. In this paper, we introduce this integrated approach and show its use for interactive web exploration of spatial and historical aggregations from data marts.

关键词： interactive web exploration geo-referenced cubes data marts spatio-temporal indexing spatial OLAP

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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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FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 1017-1024页

作者： Hurter, Christophe Tissoires, Benjamin Conversy, Stephane DSNA/DTI R&D ENAC and IRIT/IHCS ENAC and IRIT/IHCS

When displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertin's visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data.

关键词： visualization iterative exploration direct manipulation trajectories

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ActiviTree: Interactive visual exploration of Sequences in Event-Based data Using Graph Similarity

ActiviTree: Interactive Visual Exploration of Sequences in E...

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IEEE Information visualization conference/IEEE visualization conference

作者： Vrotsou, Katerina Johansson, Jimmy Cooper, Matthew Linkoping Univ S-58183 Linkoping Sweden

关键词： Interactive visual exploration event-based data sequence identification graph similarity node similarity

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A data Decorrelation Method for 3D Position Measurements from a Line Sensors Based Photogrammetric Measuring System

A Data Decorrelation Method for 3D Position Measurements fro...

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2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2010)

作者： Yu Lin Xiao-Wei Tu Claude Perron Fengfeng Xi Department of Aerospace Engineering Ryerson University Toronto Canada Aerospace Manufacturing Technology Center National Research Council Canada Montreal Canada

In this study, a data decorrelation method is developed for processing 3D position measurements obtained by a line sensors based photogrammetric measuring system. The measured 3D data suffer from correlations due to the principle of the triangulation. This nature causes complexity when a calibration procedure is carried out. Additionally, the correlated data may affect its further exploration and applications, such as robot visual servoing. For this reason, a data decorrelation method based on multivariate statistical analysis is proposed to preprocess the 3D coordinate measurements, in such a way that each component of the coordinate can be treated individually. Consequently, the calibration procedure can be significantly simplified in a less time and memory consuming manner. The proposed method includes the local and global data decorrelation. Evaluation and comparisons of these two approaches have been analyzed with a conclusion on the better performance of the global data decorrelation. The optimal decorrelated configuration based on the results has been identified for the measuring system.

关键词： Decorrelation Correlation Three dimensional displays Sensors Covariance matrix Ellipsoids Calibration

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VIAssist: visual Analytics for Cyber Defense

VIAssist: Visual Analytics for Cyber Defense

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IEEE conference on Technologies for Homeland Security

作者： Goodall, John R. Sowul, Mark Secure Decisions division of Applied Visions Inc. Applied Visions Inc. United States

ISBN: (纸本)9781424441785

analysis of voluminous computer network data has become a common practice for cyber defense, but few tools provide adequate support for cyber-infrastructure defenders' workflow, visual exploration, IP geo-location, scalability, collaboration, or reporting. The state-of-the-art in visual analysis tools for cyber defense is typically no more than spreadsheets and primitive charting. While familiar to users, this approach ignores the human perceptual ability to identify novel patterns and anomalies when data is presented graphically. This paper reports on a visual analytics systems, VIAssist, being developed for cyber-infrastructure protection that helps cyber defenders better understand the massive, multi-dimensional datasets to protect our nation's critical infrastructure.

关键词： visual analytics visualization site security monitoring computer network security

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A visual Method for High-Dimensional data Cluster exploration

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16th International conference on Neural Information Processing (ICONIP 2009)

作者： Zhang, Ke-Bing Huang, Mao Lin Orgun, Mehmet A. Nguyen, Quang Vinh Macquarie Univ Dept Comp Sydney NSW 2109 Australia Univ Western Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Univ Western Sydney Sch Comp & Math Penrith NSW 1797 Australia

ISBN: (纸本)9783642106828

visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration.

关键词： Cluster exploration visualization Statistics

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visual Human plus Machine Learning

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 1327-1334页

作者： Fuchs, Raphael Waser, Juergen Groeller, Meister Eduard ETH Zurich Switzerland VRVis Vienna Vienna Austria TU Vienna Vienna Austria

In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combines the vast search and processing power of the computer with the superior reasoning and pattern recognition capabilities of the human user. An evolutionary search algorithm has been adapted to assist in the fuzzy logic formalization of hypotheses that aim at explaining features inside multivariate, volumetric data. Up to now, users solely rely on their knowledge and expertise when looking for explanatory theories. However, it often remains unclear whether the selected attribute ranges represent the real explanation for the feature of interest. Other selections hidden in the large number of data variables could potentially lead to similar features. Moreover, as simulation complexity grows, users are confronted with huge multidimensional data sets making it almost impossible to find meaningful hypotheses at all. We propose an interactive cycle of knowledge-based analysis and automatic hypothesis generation. Starting from initial hypotheses, created with linking and brushing, the user steers a heuristic search algorithm to look for alternative or related hypotheses. The results are analyzed in information visualization views that are linked to the volume rendering. Individual properties as well as global aggregates are visually presented to provide insight into the most relevant aspects of the generated hypotheses. This novel approach becomes computationally feasible due to a GPU implementation of the time-critical parts in the algorithm. A thorough evaluation of search times and noise sensitivity as well as a case study on data from the automotive domain substantiate the usefulness of the suggested approach.

关键词： Interactive visual analysis Volumetric data Multiple Competing Hypotheses Knowledge Discovery Computer-assisted Multivariate data exploration Curse of Dimensionality Predictive analysis Genetic Algorithm

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