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10th Annual conference on visualization and data analysis

作者： Lee, SJ Hahn, JK Powell, AM Greene, G George Washington Univ Inst Comp Graph Washington DC 20052 USA

ISBN: (纸本)0819448095

This paper presents a visual information exploration tool called INSPECT. INSPECT provides geospatial information analysts with an effective way to visually filter multidimensional data and explore the underlying information contained within it. In geospatial intelligence information analyses, it is necessary to query, visualize and understand the data combined with location information. These operations are not simple since they include complex database queries of both spatial and non-spatial data. Moreover, analysts need to repeatedly query and visualize data until they reach a desirable conclusion. Using INSPECT, analysts are able to experimentally query the database avoiding complex database schema and visualize the results in geospatial context with minimal effort. The tools available with INSPECT include see-through lens visualization, relationship visualization, time varying analysis, saved lens-filter sessions, a data reachback capability, and iterative visual exploration.

关键词： information visualization visual query lens filter visual analysis

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visual-PERCEPTION OF RETROREFLECTIVE OBJECTS THROUGH LIGHT-SCATTERING MEDIA 2

VISUAL-PERCEPTION OF RETROREFLECTIVE OBJECTS THROUGH LIGHT-S...

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conference on visual data exploration and analysis ii

作者： BARUN, VV BELARUS ACAD SCI INST PHYS SCATTERING MEDIA OPT LAB MINSK 220072 BELARUS

ISBN: (纸本)0819417572

Vision characteristics are covered by the image transfer theory. But up to now, it dealt mainly with observation of Lambertian (i.e., diffuse-reflecting) objects on a Lambertian background. This model of reflection is quite a reasonable one for many natural and artificial objects to describe vision quality. This paper presents the mathematical description for vision criteria of another class of objects-retroreflectors to permit their angular patterns of reflection to be dealt with under unfavorable observation conditions through a light-scattering medium, such as fog. the small-angle diffusion approximation is used for the calculations of light characteristics under illumination by some source of an active vision system. by way of examples, there will be considered two questions: (1) visual perception of large-area objects where some parts of a retroreflector can be seen as dark and others as bright ones. This fact may be important when analyzing and exploring visual information being read out from a retroreflective panel. (2) The interesting effect of enhancing the contrast of a retroreflector image with increasing optical thickness of a scattering medium. This is related to increasing 'effective' albedo of an 'equivalent' Lambertian object the retroreflector can be replaced by. The results on vision characteristics of retroreflective objects are compared with those for the case of observation of Lambertian ones. The corresponding differences are discussed.

关键词： RETROREFLECTOR LAMBERTIAN REFLECTOR VIEWING MULTIPLE SCATTERING CONTRAST ACTIVE VISION SYSTEM

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An Interactive visualization System for Near-Earth Space exploration data 4

An Interactive Visualization System for Near-Earth Space Exp...

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4th International conference on Environmental Science and Material Application (ESMA)

作者： Yu, Haijun Li, Shengyang Zhang, Tao Univ Chinese Acad Sci CAS Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing Peoples R China

Aiming at the actual needs of efficient and convenient visual analysis of near-Earth space exploration data, this paper studies the functional realization mode, data flow and interactive method, analyzes the functional framework and data flow of the interactive visualization system of near-Earth space exploration data in detail, designs a multi-level, loosely coupled and easily extensible system architecture, and focuses on solving the logic model design, the fusion rendering of the multivariable data, the shadow calculation, the data clustering analysis, and other key technologies. The application of the system has realized the interactive and visual analysis of the massive and multi-source near-Earth space exploration data, and provided convenient data analysis service for the vast number of scientific research and application users, which helps to better play the potential value of near-Earth space exploration data.

关键词： Space research

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Big data exploration through visual Analytics

Big Data Exploration through Visual Analytics

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Abousalh-Neto, Nascif A. Kazgan, Sumeyye SAS Institute United States

ISBN: (纸本)9781467347532

SAS (R) visual Analytics Explorer is an advanced data visualization and exploratory data analysis application that is a component of the SAS visual Analytics solution. It excels at handling big data problems like the VAST challenge. With a wide range of visual analytics features and the ability to scale to massive datasets, SAS visual Analytics Explorer enables analysts to find patterns and relationships quickly and easily, no matter the size of their data. In this summary paper, we explain how we used SAS visual Analytics Explorer to solve the VAST Challenge 2012 mini-challenge 1.

关键词： visual analytics big data data visualization exploratory data analysis

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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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SOM Flow: Guided Exploratory Cluster analysis with Self-Organizing Maps and Analytic Provenance

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 120-130页

作者： Sacha, Dominik Kraus, Matthias Bernard, Juergen Behrisch, Michael Schreck, Tobias Asano, Yuki Keim, Daniel A. Univ Konstanz Constance Germany Tech Univ Darmstadt Darmstadt Germany Graz Univ Technol Graz Austria Univ Tubingen Tubingen Germany

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

关键词： visual Analytics Interaction visual Cluster analysis Quality Metrics Guidance Self -Organizing Maps Time Series

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Interactive data exploration through Multiple visual Contexts with Different data Models and Dimensions 21

Interactive Data Exploration through Multiple Visual Context...

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21st International conference on Information visualisation (IV)

作者： Phi Giang Pham Huang, Mao Lin Quang Vinh Nguyen Univ Technol Sydney NSW Australia Cantho Tech Econ Coll Can Tho Vietnam Western Sydney Univ Sydney NSW Australia

ISBN: (纸本)9781538608319

visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge.

关键词： visual analytics dynamic visual query interactive user interface

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MTDES: Multi-dimensional Temporal data exploration System;Strong Support for Exploratory analysis Award in VAST 2018, Mini-Challenge 2 13

<i>MTDES</i>: Multi-dimensional Temporal Data Exploration Sy...

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Pham, Vung V. Dang, Tommy Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781538668610

This work proposes a visual analytic solution which is well-designed to provide investigative functions with fluent interactions to analyze multi-dimensional temporal data. The solution allows users to view different dimensions of the data at different levels of details with a well-designed mixture of different visualizations and smooth interactions. At the general/overview level, various aggregation strategies are used to reduce data to be visualized, and different sorting procedures are used to cluster correlated data together to help discover patterns. Detail views are provided to explore and confirm/reject the identified patterns. Interaction and smooth transition between views are implemented to enable natural actions while performing analysis tasks. This work also presents the result of applying the solution to the VAST 2018 - Mini-Challenge (MC) 2 dataset, which led to the Strong Support for Exploratory analysis award for the challenge.

关键词： Multi-dimensional, temporal data analysis-Heat-map-Line-graph-Interaction Design VAST Challenge 2018-Mini-Challenge 2-Award: Strong Support for Exploratory analysis

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BrainTrawler: A visual analytics framework for iterative exploration of heterogeneous big brain data

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COMPUTERS & GRAPHICS-UK 2019年 82卷 304-320页

作者： Ganglberger, Florian Swoboda, Nicolas Frauenstein, Lisa Kaczanowska, Joanna Haubensak, Wulf Buehler, Katja VRVis Res Ctr Donau City Str 11-13 A-1220 Vienna Austria Vienna Bioctr VBC Res Inst Mol Pathol IMP Campus Vienna Bioctr 1 A-1030 Vienna Austria

In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data brain-network graphs with billions of edges and vast spatial gene expression resources the representation of tens of thousands genes in brain space. Their joint analysis for higher order relations in structural or functional neuroanatomy would enable the genetic dissection of brain networks on a genome-wide scale. Current experimental workflows involve only time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. In this paper, we propose BrainTrawler, a task-driven, web-based framework that incorporates visual analytics methods to explore heterogeneous neurobiological data. It facilitates spatial indexing to query large-scale voxel-level connectivity data and gene expression collections in real-time. Relating data to the hierarchical structure of common anatomical atlases enables the retrieval on different anatomical levels. Together with intuitive network visualization, iterative visual queries, and quantitative information this allows the genetic dissection of multimodal networks on local/global scales in a spatial context. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning related functional neuroanatomy in mice. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Big data Networks Neuroscience Gene expression Brain parcellation visual queries

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Interactive visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media data

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 270-279页

作者： Chen, Siming Yuan, Xiaoru Wang, Zhenhuang Guo, Cong Liang, Jie Wang, Zuchao Zhang, Xiaolong (Luke) Zhang, Jiawan Peking Univ Sch EECS Minist Educ Key Lab Machine Percept Beijing Peoples R China Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

关键词： Spatial temporal visual analytics Geo-tagged social media Sparsely sampling Uncertainty. Movement

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