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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Seebacher, Daniel Schneider, Bruno Behrisch, Michael Univ Konstanz Constance Germany Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781538631638

We present an approach developed in course of the VAST 2017 Mini-Challenge 2. To help the ornithologist Mitch to investigate the noxious gases emitted by the four companies south of the nature preserve, we employ a combination of interactive visualizations that allow for an exploration of the data. In this paper, we present our visual-interactive approach for analyzing suspicious patterns in the data. By taking the wind data into consideration, as well, our approach allows the retrieval of patterns in the chemical releases and identify key polluters.

关键词： Chemicals Monitoring Companies Wind speed visualization Task analysis Time series analysis

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visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation

Visual Analytic Design for Characterizing Air-Sampling Senso...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Quadri, Ghulam Jilani Tuladhar, Anwesh Malla, Sulav Rosen, Paul Univ S Florida Dept Comp Engn Tampa FL 33620 USA

ISBN: (纸本)9781538631638

analysis and exploration of similar continuous data for various air-sampler sensor have been performed by developing a processing tool. The analysis and design for characterizing sensor data have been stated and described. Continuous 24X7 sensor data and metrological data leads the layout choices for the analytical design. Such design choices are helpful to understand the pattern of similar reading for various devices at a time. In this paper, we describe the use of the mentioned design choices for to identify pattern, unusual behavior. We used this tool and design choice to solve VAST 2017 mini challenge 2.

关键词： Air-sampler vector dot-product multi-label data multi-variate data

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visual analysis of Confocal Raman Spectroscopy data using Cascaded Transfer Function Design

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COMPUTER GRAPHICS FORUM 2017年第3期36卷 239-249页

作者： Schikora, Christoph M. Plack, Markus Bornemann, Rainer Bolivar, Peter Haring Kolb, Andreas Univ Siegen Comp Graph & Multimedia Syst Siegen Germany Univ Siegen High Frequency & Quantum Elect Siegen Germany

2D Confocal Raman Microscopy (CRM) data consist of high dimensional per-pixel spectral data of 1000 bands and allows for complex spectral and spatial-spectral analysis tasks, i.e., in material discrimination, material thickness, and spatial material distributions. Currently, simple integral methods are commonly applied as visual analysis solutions to CRM data which exhibit restricted discrimination power in various regards. In this paper we present a novel approach for the visual analysis of 2D multispectral CRM data using multi-variate visualization techniques. Due to the large amount of data and the demand of an explorative approach without a-priori restriction, our system allows for arbitrary interactive (de)selection of varaibles w/o limitation and an unrestricted online definition/construction of new, combined properties. Our approach integrates CRM specific quantitative measures and handles material-related features for mixed materials in a quantitative manner. Technically, we realize the online definition/construction of new, combined properties as semi-automatic, cascaded, 1D and 2D multidimensional transfer functions (MD-TFs). By interactively incorporating new (raw or derived) properties, the dimensionality of the MD-TF space grows during the exploration procedure and is virtually unlimited. The final visualization is achieved by an enhanced color mixing step which improves saturation and contrast.

关键词： Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Display algorithm I.4.10 [Image Processing and Computer Vision]: Multidimensional I.5.3 [Pattern Recognition]: Algorithms

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Temporal Pattern analysis and Source Detection through visual analysis on Multi-Dimensional Time Series data

Temporal Pattern Analysis and Source Detection through Visua...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Jiang, Ruike Huang, Wei Ma, Nan Hong, Fan Zhao, Ying Yuan, Xiaoru Peking Univ Key Lab Machine Percept Minist Educ Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Peking Univ Beijing Engn Technol Res Ctr Virtual Simulat & Vi Beijing Peoples R China QIHOO 360 Beijing Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

In VAST Challenge 2017, we developed a visual exploration system for detection of sensor anomaly, pattern of chemical distribution and responsible factory for each release of chemical. In this report, we discuss detai... 详细信息

ISBN: (纸本)9781538631638

关键词： Chemicals Production facilities visualization Time series analysis data preprocessing Monitoring ISO

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An Interactive visualization System for Near-Earth Space exploration data

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IOP conference Series: Earth and Environmental Science 2019年第5期252卷

作者： Haijun Yu Shengyang Li Tao Zhang Key Laboratory of Space Utilization Technology and Engineering Center for space Utilization CAS Universit of Chinese Academy of Sciences Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for space Utilization CAS Beijing China Technology and Engineering Center for space Utilization CAS Beijing 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.

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SparQs: visual Analytics for Sparking Creativity in Social Media exploration 9th

SparQs: Visual Analytics for Sparking Creativity in Social M...

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9th International conference on Social Computing and Social Media (SCSM) Held as Part of 19th International conference on Human-Computer Interaction (HCI International)

作者： Chen, Nan-Chen Brooks, Michael Kocielnik, Rafal Hong, Sungsoo (Ray) Smith, Jeff Lin, Sanny Qu, Zening Aragon, Cecilia Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9783319585628;9783319585611

Social media has become a fruitful platform on which to study human behavior and social phenomena. However, social media data are usually messy, disorganized, and noisy, which makes finding patterns in such data a challenging task. visualization can help with the exploration of such massive data. Researchers studying social media often begin by reviewing related research. In this paper, we consider the idea that information from related research can be incorporated into social media visualization tools in order to spark creativity and guide exploration. To develop an effective overview of social media research with which to seed our tool, we conducted a content analysis of social media related papers and designed SparQs, a visual analytics tool to spark creativity in social media exploration. We conducted a pilot evaluation with three social media researchers as well as a participatory design workshop to explore further directions.

关键词： visualization visual analytics Social media Exploratory analysis Research questions Social science

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Temporal Summary Images: An Approach to Narrative visualization via Interactive Annotation Generation and Placement

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 511-520页

作者： Bryan, Chris Ma, Kwan-Liu Woodring, Jonathan Univ Calif Davis Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM USA

visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POs). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.

关键词： Narrative visualization storytelling annotations comic strip visualization time-varying data

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Quantitative Externalization of visual data analysis Results Using Local Regression Models 1st

Quantitative Externalization of Visual Data Analysis Results...

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1st IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain conference on Machine Learning and Knowledge Extraction (CD-MAKE)

作者： Matkovic, Kresimir Abraham, Hrvoje Jelovic, Mario Hauser, Helwig VRVis Res Ctr Vienna Austria AVL AST Doo Zagreb Croatia Univ Bergen Bergen Norway

ISBN: (纸本)9783319668086;9783319668079

Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

关键词： Interactive visual data exploration and analysis Local regression models Externalization of analysis results

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dataScope: Interactive visual Exploratory Dashboards For Large Multidimensional data 8

DataScope: Interactive Visual Exploratory Dashboards For Lar...

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8th IEEE Annual Workshop on visual Analytics in Healthcare (VAHC)

作者： Iyer, Ganesh DuttaDuwarah, Sapoonjyoti Sharma, Ashish Emory Univ Dept Biomed Informat Atlanta GA 30322 USA

ISBN: (纸本)9781538631874

dataScope is a web-based tool for generating interactive visual dashboards on large scale multidimensional datasets. Users create these dashboards using a high-level declarative grammar, and use them to explore data and create cohorts for downstream analysis. We describe dataScope's architecture, design considerations and provide an overview of the system. We highlight some of dataScope's features that were useful in the case studies using datasets from cancer registries and co-clinical trials. In benchmarks dataScope is able to perform sub-second queries on data sizes ranging from thousand to million records.

关键词： Information visualization data exploration Interactive data exploration

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PhenoStacks: Cross-Sectional Cohort Phenotype Comparison visualizations

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 191-200页

作者： Glueck, Michael Gvozdik, Alina Chevalier, Fanny Khan, Azam Brudno, Michael Wigdor, Daniel Univ Toronto Toronto ON M5S 1A1 Canada Autodesk Res San Francisco CA 94111 USA Inria Le Chesnay France Hosp Sick Children Toronto Toronto ON Canada

Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e. g., co-occurrence) and isolate special cases (e. g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e. g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design.

关键词： Cross-sectional cohort analysis Phenotypes Human Phenotype Ontology (HPO)

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