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2018 2nd International conference on Computer Graphics and Digital Image Processing, CGDIP 2018

作者： Song, Meina Pang, Zhihui Haihong, E. Beijing University of Posts and Telecommunications Beijing100876 China

With the advent of cloud computing and big data era, high-dimensional data sets are widely available in real life. Because of the increase of data dimension and complexity, it is difficult to carry out comprehensive analysis and exploration for high-dimensional data. Therefore, we propose a new visual analysis approach to high-dimensional data. Firstly, we use the principal component analysis (PCA) to reduce the dimension of the high-dimensional data. Then, we use clustering algorithm to classify the reduced dimension data, and each class is rendered independently with different color. Finally, we use the edge binding algorithm to perform a visual clustering. The curves in the same classes are converged and in different classes are separated to alleviate visual confusion. In order to analyze the visualization results better, we also provide the visual interaction technology of «brush». The experimental result shows that our approach can help users to explore the implicit feature patterns quickly and is effective for visual analysis of large-scale high-dimensional data. © Published under licence by IOP Publishing Ltd.

关键词： Clustering algorithms

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visual analysis of Spatio-temporal Distribution and Retweet Relation in Weibo Event

Visual Analysis of Spatio-temporal Distribution and Retweet ...

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IEEE International conference on Big data and Smart Computing (BigComp)

作者： Li, Haisheng Liang, Xunge Song, Xuan Cai, Qiang Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China Beijing Key Lab Big Data Technol Food Safety Beijing 100048 Peoples R China

ISBN: (纸本)9781538636497

Sina Weibo is the most popular microblog service in China and it can provide abundant information about netizens' attitudes and opinions to those events which are exposed on the Internet. However, it is difficult to know the characteristics of internet public opinions, such as the evolution of users' focus over time, spatio-temporal distribution of users participating in event comments, weibo retweet relation, etc. To fully understand those, we propose a visual analytic system of Weibo Event, short for WeiboViz, which can be mainly divided into four subparts: fundamental information visualization, spatio-temporal distribution visualization, keywords and entities visualization, weibo retweet relation visualization. A case study of "Pseudomonas aeruginosa' exceeded in the Master Kong You Yue drinking water" demonstrates the effectiveness of the proposed system for the exploration and understanding of weibo data about specific event.

关键词： food safety event visual analytic system weibo spatio-temporal relation retweet tree

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2018 IEEE conference on visual Analytics Science and Technology, VAST 2018 - Proceedings

2018 IEEE Conference on Visual Analytics Science and Technol...

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13th IEEE conference on visual Analytics Science and Technology, VAST 2018

ISBN: (纸本)9781538668610

The proceedings contain 27 papers. The topics discussed include: the effect of proximity in social data charts on perceived unity;the effect of semantic interaction on foraging in text analysis;VUSphere: visual analysis of video utilization in online distance education;SMARTexplore: simplifying high-dimensional data analysis through a table-based visual analytics approach;EmbeddingVis: a visual analytics approach to comparative network embedding inspection;analyzing the noise robustness of deep neural networks;segue: overviewing evolution patterns of egocentric networks by interactive construction of spatial layouts;and multilevel visual clustering exploration for incomplete time-series in water samples.

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visual Insight of Spatiotemporal IoT-generated Contents 18

Visual Insight of Spatiotemporal IoT-generated Contents

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14th International conference on Advanced visual Interfaces (AVI)

作者： Lee, Jun Kim, Kyoung-Sook Lee, Ryong Lee, Sang-Hwan Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo Japan Korea Inst Sci & Technol Informat KISTI Res Data Hub Ctr Daejeon South Korea

ISBN: (纸本)9781450356169

The rapid evolution of the Internet of Things (IoT) and Big data technology has been generating a large amount and variety of sensing contents, including numeric measured values (e.g., timestamps, geolocations, or sensor logs) and multimedia (e.g., images, audios, and videos). In analyzing and understanding heterogeneous types of IoT-generated contents better, data visualization is an essential component of exploratory data analyses to facilitate information perception and knowledge extraction. This study introduces a holistic approach of storing, processing, and visualizing IoT-generated contents to support context-aware spatiotemporal insight by combining deep learning techniques with a geographical map interface. visualization is provided under an interactive web-based user interface to help the an efficient visual exploration considering both time and geolocation by easy spatiotemporal query user interface'.

关键词： IoT-generated Contents Geovisualization Spatiotemporal analysis Deep Learning Object Detection

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Cohort Representation and exploration 5

Cohort Representation and Exploration

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5th IEEE International conference on data Science and Advanced Analytics (IEEE DSAA)

作者： Omidvar-Tehrani, Behrooz Amer-Yahia, Sihem Lakshmanan, Laks V. S. Univ Grenoble Alpes Grenoble France CNRS Paris France Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781538650905

The abundant availability of health-care data calls for effective analysis methods which help medical experts gain a better understanding of their data. While the focus has been largely on prediction, "representation" and "exploration" of health-care data have received little attention. In this paper, we introduce CORE, a framework for representing and exploring patient cohorts. Obtaining a readable and succinct representation of health data of a cohort is challenging because cohorts often consist of hundreds of patients whose medical actions are of various types and occur at different points in time. We extend the Needleman-Wunsch algorithm for sequence matching to handle temporal sequences, and propose "trajectory families", a customized index to efficiently compare and aggregate patient trajectories into a cohort representation. We define cohort exploration as finding similar cohorts to a given cohort. This problem is challenging because the potential number of similar cohorts is huge. We propose a two-staged approach based on limiting the search space to "contrast cohorts" and then computing their similarity to the given cohort. To speed up cohort similarity computation, we use "event sets" in the same spirit as the double dictionary encoding proposed for keyword search. We run qualitative and quantitative experiments on real data to explore the efficiency and usefulness of CORE. We show that CORE representations reduce time-to-insight from hours to seconds and help medical experts find insights better than state-of-the-art visual Analytics tools.

关键词： Trajectory Hospitals Aggregates visual analytics Diseases Indexes Dictionaries

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EuroVis 2018 - Eurographics/IEEE VGTC conference on visualization 2018 - Short Papers

EuroVis 2018 - Eurographics/IEEE VGTC Conference on Visualiz...

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20th Eurographics/IEEE VGT conference on visualization, EuroVis 2018

ISBN: (纸本)9783038680604

The proceedings contain 21 papers. The topics discussed include: evolutionary lines for flow visualization;colored stochastic shadow mapping for direct volume rendering;visualizing functional regions by analysis of geo-textual data;visual analysis of parallel interval events;comparative visual analysis of pelvic organ segmentations;improving provenance data interaction for visual storytelling in medical imaging data exploration;ChemoExplorer: a dashboard for the visual analysis of chemotherapy response in breast cancer patients;TapVis: a data visualization approach for assessment of alternating tapping performance in patients with Parkinsonâ€™s disease;sketching temporal uncertainty - an exploratory user study;and issues and suggestions for the development of a biodiversity data visualization support tool.

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Cluster-based analysis of multi-parameter distributions in cloud simulation ensembles

Cluster-based analysis of multi-parameter distributions in c...

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2019 conference on Vision, Modeling and visualization, VMV 2019

作者： Kumpf, Alexander Stumpfegger, Josef Westermann, Rüdiger Germany

ISBN: (纸本)9783038680987

The proposed approach enables a comparative visual exploration of multi-parameter distributions in time-varying 3D ensemble simulations. To investigate whether dominant trends in such distributions occur, we consider the simulation elements in each dataset—per ensemble member and time step—as elements in the multi-dimensional parameter space, and use t-SNE to project these elements into 2D space. To find groups of elements with similar parameter values in each time step, the resulting projections are clustered via k-Means. Since elements with similar data values can be disconnected in one single projection, we compute an ensemble of projections using multiple t-SNE runs and use evidence accumulation to determine sets of elements that are stably clustered together. We build upon per-cluster multi-parameter distribution functions to quantify cluster similarity, and merge clusters in different ensemble members. By applying the proposed approach to a time-varying ensemble, the temporal development of clusters, and in particular their stability over time can be analyzed. We apply this approach to analyze a time-varying ensemble of 3D cloud simulations. The visualizations via t-SNE, parallel coordinate plots and scatter plot matrices show dependencies between the simulation results and the simulation parameters used to generate the ensemble, and they provide insight into the temporal ensemble variability regarding the major trends in the multi-parameter distributions. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Distribution functions

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visual Attention and Haptic Control: A Cross-Study

Visual Attention and Haptic Control: A Cross-Study

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IEEE International conference on Multimedia Big data (BigMM)

作者： Hong Xue Tiesong Zhao Weiling Chen Qian Liu Shaohua Zheng Chang Wen Chen Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Dept. of Computer Science and Technology Dalian University of Technology Dalian China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

The variety of multimedia big data has promoted emerging applications of multiple sensorial media (mulsemedia) types, in which haptic information attracts increasing attentions. Until now, the interaction between haptic signal and conventional audio-visual signals have not been fully investigated. In this work, we make an exploration on the cross-modal interactivity in task-driven scenarios. We first explore the correlation between visual attention and haptic control in three designed tasks: random-trajectory, fixed-trajectory and obstacle-avoidance. Then, we propose a visual-haptic interaction model that estimates kinesthetic position of haptic control with the information of gaze only. By incorporating a Long Short-Term Memory (LSTM) neural network, the proposed model provides effective prediction in the scenarios of fixed-trajectory and obstacle-avoidance, with its performance superior to other selected machine learning-based models. To further examine our model, we execute it in a haptic control task using visual guidance. Implementation results show a high task achievement rate.

关键词： Task analysis Haptic interfaces visualization Correlation Software Tracking Process control

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EuroVis 2018 - Eurographics/IEEE VGTC conference on visualization 2018, Posters

EuroVis 2018 - Eurographics/IEEE VGTC Conference on Visualiz...

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20th Eurographics/IEEE VGTC conference on visualization, EuroVis 2018

ISBN: (纸本)9783038680659

The proceedings contain 15 papers. The topics discussed include: CV3: visual exploration, assessment, and comparison of CVs;extending document exploration with image retrieval: concept and first results;visually exploring data provenance and quality of open data;case studies of shareable personal map visualization;an eye-tracking study on sparklines within textual context;network analysis for financial fraud detection;validation of quantitative measures for edge bundling by comparing with human feeling;exploring uncertainty in image segmentation ensembles;supporting visual parameter analysis of time series segmentation with correlation calculations;the impact of visualizing uncertainty on train trip selection;and categorizing uncertainties in the process of segmenting and labeling time series data.

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LDSScanner: Exploratory analysis of Low-Dimensional Structures in High-Dimensional datasets

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 236-245页

作者： Xia, Jiazhi Ye, Fenjin Chen, Wei Wang, Yusi Chen, Weifeng Ma, Yuxin Tung, Anthony K. H. Cent South Univ Changsha Hunan Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Zhejiang Univ Finance & Econ Hangzhou Zhejiang Peoples R China Natl Univ Singapore Singapore Singapore

Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.

关键词： High-dimensional data low-dimensional structure subspace manifold visual exploration

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