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Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2007年第6期13卷 1176-1183页

作者： Wood, Jo Dykes, Jason Slingsby, Aidan Clarke, Keith City Univ London Dept Informat Sci London England Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here.

关键词： large dataset visualization text and document visualization multiresolution visualization geographic visualization applications of infovis

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Everyday life discoveries: Mining and visualizing activity patterns in social science diary data

Everyday life discoveries: Mining and visualizing activity p...

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11th International conference on Information visualization (iv 2007)

作者： Vrotsou, Katerina Ellegard, Kajsa Cooper, Matthew Linkoping Univ Norrkoping Visualizat & Interact Studio Linkoping Sweden Linkoping Univ TEMA Technol & Social Change Linkoping Sweden

ISBN: (纸本)9780769529004

The ability to identify and examine patterns of activities is a key tool for social and behavioural science. In the past this has been done by statistical or purely visual methods but automated sequential pattern analysis through sophisticated data mining and visualization tools for pattern location. and evaluation can open up new possibilities for interactive exploration of the data. This paper describes the addition of a sequential pattern identification method to the visual activity-analysis tool, visual-TimePAcTS, and its effectiveness in the process of pattern. analysis in social science diary data. The results have shown that the method correctly identifies patterns and conveys them effectively to the social scientist in a manner that allows them quick and easy understanding of the significance of the patterns.

关键词： data mining

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Approach to clustering large visual databases using wavelet transform

Approach to clustering large visual databases using wavelet ...

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conference on visual data exploration and analysis iv

作者： Sheikholeslami, G Zhang, AD SUNY/Buffalo (United States)

ISBN: (纸本)0819424285

Many applications demand the capability of retrieval based on image content. A classification mechanism is needed to categorize images based on feature. similarity. An effective classification of the images can support efficient retrieval of images, In this paper, we investigate a feature-based approach to image clustering and retrieval. Four different texture-based feature sets of images are extracted using Haar and Daubechies wavelet transforms. Using multiresolution property of wavelets, we extract the features at different levels. The experimental results of our clustering approach on air photo images are reported.

关键词： clustering image database multi-resolution wavelet transform

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Tractome: a visual data mining tool for brain connectivity analysis

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data MINING AND KNOWLEDGE DISCOVERY 2015年第5期29卷 1258-1279页

作者： Porro-Munoz, Diana Olivetti, Emanuele Sharmin, Nusrat Thien Bao Nguyen Garyfallidis, Eleftherios Avesani, Paolo Bruno Kessler Fdn NeuroInformat Lab NILab Trento Italy Univ Trento Ctr Interdipartimentale Mente & Cervello CIMeC I-38123 Mattarello TN Italy Univ Sherbrooke Dept Comp Sci Sherbrooke PQ J1K 2R1 Canada

Diffusion magnetic resonance imaging data allows reconstructing the neural pathways of the white matter of the brain as a set of 3D polylines. This kind of data sets provides a means of study of the anatomical structures within the white matter, in order to detect neurologic diseases and understand the anatomical connectivity of the brain. To the best of our knowledge, there is still not an effective or satisfactory method for automatic processing of these data. Therefore, a manually guided visual exploration of experts is crucial for the purpose. However, because of the large size of these data sets, visual exploration and analysis has also become intractable. In order to make use of the advantages of both manual and automatic analysis, we have developed a new visual data mining tool for the analysis of human brain anatomical connectivity. With such tool, humans and automatic algorithms capabilities are integrated in an interactive data exploration and analysis process. A very important aspect to take into account when designing this tool, was to provide the user with comfortable interaction. For this purpose, we tackle the scalability issue in the different stages of the system, including the automatic algorithm and the visualization and interaction techniques that are used.

关键词： Brain connectivity analysis visual data mining Scalability Dissimilarity representation Clustering

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In Situ exploration of Large Dynamic Networks

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2334-2343页

作者： Hadlak, Steffen Schulz, Hans-Joerg Schumann, Heidrun Univ Rostock D-2500 Rostock 1 Germany Graz Univ Technol A-8010 Graz Austria

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e. g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization - a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown - right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.

关键词： Dynamic graph data multiform visualization multi-focus plus context

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Towards a visual Approach for Representing Analytical Provenance in exploration Processes 25

Towards a Visual Approach for Representing Analytical Proven...

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25th International conference Information visualisation (iv) - AI and visual Analytics and data Science

作者： Menin, Aline Cava, Ricardo Dal Sasso Freitas, Carla Maria Corby, Olivier Winckler, Marco Univ Cote dAzur CNRS INRIA I3S UMR 7271 Sophia Antipolis France Sul Rio Grandense Fed Inst Pelotas RS Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9781665438278

visualization techniques are useful tools to explore data by enabling the discovery of meaningful patterns and causal relationships. The discovery process is often exploratory and requires multiple views to support analyzing different or complementary perspectives to the data. In this context, analytic provenance shows great potential to understand users' reasoning process through the study of their interactions on multiple view systems. In this paper, we present an approach based on the concept of chained views to support the incremental exploration of large, multidimensional datasets. Our goal is to provide visual representation of provenance information to enable users to retrace their analytical actions and to discover alternative exploratory paths without loosing information on previous analyses. We demonstrate that our implementation of the approach, MGExplorer (Multidimensional Graph Explorer), allows users to explore different perspectives to a dataset by modifying the input graph topology, choosing visualization techniques, arranging the visualization space in meaningful ways to the ongoing analysis and retracing their analytical actions. MGExplorer combines multiple visualization techniques and visual querying while representing provenance information as segments connecting views, which each supports selection operations that help define subsets of the current dataset to be explored by a different view. We demonstrate the usage of the tool through a study case where we explore co-authorship data. We assess the approach through performance metrics, temporal ordering of tasks, number of physical actions, and amount of information to be recalled in-between actions applied to the chosen visual exploration scenarios using chained views.

关键词： Analytical provenance Multiple views Chained views Multidimensional data exploration Provenance visualization

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An interaction model for visualization systems utilizing color and sound representations

An interaction model for visualization systems utilizing col...

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conference on visual data exploration and analysis iv

作者： Seetharaman, K Grinstein, G Levkowitz, H UNIV MASSACHUSETTS DEPT COMP SCILOWELLMA

ISBN: (纸本)0819424285

We present a conceptual model for interaction in exploratory data visualization systems. This model extends interaction to systems that use geometry, texture, color, and sound to present data to the user. Such systems need to be highly interactive and user centered. We extend interaction to utilize not only geometry, but also color and sound representations of data. Our conceptual model supports interaction in multiple representational spaces. A representational space is a space, such as a color space or a sound space, that is used in the representation of data. The model extends the conventional visualization output pipeline, separating the data from its representations. Interaction operations can be performed on either part of the pipeline.

关键词： interaction visualization model graphics pipeline data representations color sound

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Asymmetric Relations in Longitudinal Social Networks

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2283-2290页

作者： Brandes, Ulrik Nick, Bobo Univ Konstanz Dept Comp & Informat Sci D-7750 Constance Germany

In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animations and small multiples of intermediate states, depending on the type of media available. We present an alternative approach based on matrix representation of gestaltlines (a combination of Tufte's sparklines with glyphs based on gestalt theory). As a result, we obtain static, compact, yet data-rich diagrams that support specifically the exploration of evolving dyadic relations and persistent group structure, although at the expense of cross-sectional network views and indirect linkages.

关键词： Network visualization Social Networks Time Series data visual Knowledge Discovery and Representation Glyph-based Techniques

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visual exploration of Topics in Multimedia News Corpora 23

Visual Exploration of Topics in Multimedia News Corpora

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23rd International conference on the Information visualisation (iv) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： John, Markus Kurzhals, Kuno Ertl, Thomas Univ Stuttgart Inst Visualizat & Interact Syst VIS Stuttgart Germany Swiss Fed Inst Technol Inst Cartog & Geoinformat Zurich Switzerland

ISBN: (纸本)9781728128382

The increasing availability of digital multimedia content has led to the need of new approaches for the analysis of large databases containing video and associated data, for example, subtitles. visualization provides valuable insights of such dataset, complementing approaches solely based on techniques for knowledge discovery in databases and information retrieval. Hence, visual analytics, combining automatic processing with interactive data visualization, has proven to be an effective means to explore and interpret such data. The analysis of news corpora represents a typical task for such a scenario. Domain experts such as journalists and social science scholars require an overview of important topics, the temporal coherence of events, and they should be able to compare different topics. We present a visual analytics approach that aims to support these tasks with automatic video preprocessing, topic extraction, clustering, and dimensionality reduction. Coordinated linked views support the flexible inspection of the dataset and the processed results. We further discuss the application of our approach in a usage scenario, inspecting the dataset of a daily news broadcast of the year 2015.

关键词： visual analytics multimedia analysis

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visual debugging of data and operations for concurrent programs

Visual debugging of data and operations for concurrent progr...

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conference on visual data exploration and analysis iv

作者： Erbacher, RF Univ. of Massachusetts/Lowell (United States)

ISBN: (纸本)0819424285

Symmetric multiprocessor systems (SMPs) are the desktop systems of the future. As they become more widely used, application developers will need to make better use of them. Merely providing threading will be insufficient. Companies will need to provide applications that parallelize portions of code and use the processors in SMPs To their full extent. This will have a much more dramatic effect on improving performance. Before this can be achieved however, tools must be developed which aid programmers, who may have little or no experience in programming parallel systems, to debug and performance tune their applications. Our goal is to improve the technology available through interactive computational steering and to develop techniques that will be valuable for all types of concurrent systems not just SMPs. This will be done through integrated operation visualization, data highlighting, interactive computational steering, and by concentrating an the data in a parallel system rather than on the processors or code.

关键词： visualization interactive computational steering program visualization concurrent systems

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