Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00035
Cheng Li, J. Moortgat, Han-Wei Shen
Occlusion management is an important task for three dimension data exploration. For egocentric data exploration, the occlusion problems, caused by the camera being too close to opaque data elements, have not been well addressed by previous studies. In this paper, we propose an automatic approach to resolve these problems and provide an occlusion free egocentric data exploration. Our system utilizes a state transition model to monitor both the camera and the data, and manages the initiation, duration, and termination of deformation with animation. Our method can be applied to multiple types of scientific datasets, including volumetric data, polygon mesh data, and particle data. We demonstrate our method with different exploration tasks, including camera navigation, isovalue adjustment, transfer function adjustment, and time varying exploration. We have collaborated with a domain expert and received positive feedback.
{"title":"An Automatic Deformation Approach for Occlusion Free Egocentric Data Exploration","authors":"Cheng Li, J. Moortgat, Han-Wei Shen","doi":"10.1109/PacificVis.2018.00035","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00035","url":null,"abstract":"Occlusion management is an important task for three dimension data exploration. For egocentric data exploration, the occlusion problems, caused by the camera being too close to opaque data elements, have not been well addressed by previous studies. In this paper, we propose an automatic approach to resolve these problems and provide an occlusion free egocentric data exploration. Our system utilizes a state transition model to monitor both the camera and the data, and manages the initiation, duration, and termination of deformation with animation. Our method can be applied to multiple types of scientific datasets, including volumetric data, polygon mesh data, and particle data. We demonstrate our method with different exploration tasks, including camera navigation, isovalue adjustment, transfer function adjustment, and time varying exploration. We have collaborated with a domain expert and received positive feedback.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00027
Jun Tao, Lei Shi, Zhou Zhuang, Congcong Huang, Rulei Yu, Purui Su, Chaoli Wang, Yang Chen
Detecting, analyzing and reasoning collective anomalies is important for many real-life application domains such as facility monitoring, software analysis and security. The main challenges include the overwhelming number of low-risk events and their multifaceted relationships which form the collective anomaly, the diversity in various data and anomaly types, and the difficulty to incorporate domain knowledge in the anomaly analysis process. In this paper, we propose a novel concept of high-order correlation graph (HOCG). Compared with the previous correlation graph definition, HOCG achieves better user interactivity, computational scalability, and domain generality through synthesizing heterogeneous types of nodes, attributes, and multifaceted relationships in a single graph. We design elaborate visual metaphors, interaction models, and the coordinated multiple view based interface to allow users to fully unleash the visual analytics power over HOCG. We conduct case studies in two real-life application domains, i.e., facility monitoring and software analysis. The results demonstrate the effectiveness of HOCG in the overview of point anomalies, detection of collective anomalies, and reasoning process of root cause analysis.
{"title":"Visual Analysis of Collective Anomalies Through High-Order Correlation Graph","authors":"Jun Tao, Lei Shi, Zhou Zhuang, Congcong Huang, Rulei Yu, Purui Su, Chaoli Wang, Yang Chen","doi":"10.1109/PacificVis.2018.00027","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00027","url":null,"abstract":"Detecting, analyzing and reasoning collective anomalies is important for many real-life application domains such as facility monitoring, software analysis and security. The main challenges include the overwhelming number of low-risk events and their multifaceted relationships which form the collective anomaly, the diversity in various data and anomaly types, and the difficulty to incorporate domain knowledge in the anomaly analysis process. In this paper, we propose a novel concept of high-order correlation graph (HOCG). Compared with the previous correlation graph definition, HOCG achieves better user interactivity, computational scalability, and domain generality through synthesizing heterogeneous types of nodes, attributes, and multifaceted relationships in a single graph. We design elaborate visual metaphors, interaction models, and the coordinated multiple view based interface to allow users to fully unleash the visual analytics power over HOCG. We conduct case studies in two real-life application domains, i.e., facility monitoring and software analysis. The results demonstrate the effectiveness of HOCG in the overview of point anomalies, detection of collective anomalies, and reasoning process of root cause analysis.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131528223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid growing advancement of animation technologies, 3D animated meshes are becoming one of the major data in the industry such as virtual reality. However, treating the animated mesh data efficiently remains a challenging task due to its large scale and limited feature descriptors. In this paper, we present an evolutionary signature for animated meshes based on tempo-spatial segmentation. In specific, we first conduct temporal segmentation to a given animated meshes with sub-motions, then apply spatial segmentation within each temporal segment, and intersect spatial segmentation result for over segmentation. Thirdly, we represent the segmentation results into graphs. Finally, we devise an edge evolution matrix based on the dynamic behaviour of each edge for the evolutionary signature of the input animated mesh. Our experimental results on similarity measurement by using the proposed signature reflect the effectiveness of our method.
{"title":"An Evolutionary Signature for Animated Meshes","authors":"Guoliang Luo, Haopeng Lei, Yugen Yi, Yuhua Li, Chuahua Xian","doi":"10.1109/PacificVis.2018.00038","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00038","url":null,"abstract":"With the rapid growing advancement of animation technologies, 3D animated meshes are becoming one of the major data in the industry such as virtual reality. However, treating the animated mesh data efficiently remains a challenging task due to its large scale and limited feature descriptors. In this paper, we present an evolutionary signature for animated meshes based on tempo-spatial segmentation. In specific, we first conduct temporal segmentation to a given animated meshes with sub-motions, then apply spatial segmentation within each temporal segment, and intersect spatial segmentation result for over segmentation. Thirdly, we represent the segmentation results into graphs. Finally, we devise an edge evolution matrix based on the dynamic behaviour of each edge for the evolutionary signature of the input animated mesh. Our experimental results on similarity measurement by using the proposed signature reflect the effectiveness of our method.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127032870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00030
Alma Cantu, Thierry Duval, O. Grisvard, G. Coppin
In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIGINT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.
{"title":"HeloVis: A Helical Visualization for SIGINT Analysis Using 3D Immersion","authors":"Alma Cantu, Thierry Duval, O. Grisvard, G. Coppin","doi":"10.1109/PacificVis.2018.00030","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00030","url":null,"abstract":"In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIGINT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00034
T. Gschwandtner, Oliver Erhart
Sensible data analysis requires data quality control. An essential part of this is data profiling, which is the identification and assessment of data quality problems as a prerequisite for adequately handling these problems. Differentiating between actual quality problems and unusual, but valid data values requires the "human-in-the-loop" through the use of visual analytics. Unfortunately, existing approaches for data profiling do not adequately support the special characteristics of time, which is imperative to identify quality problems in time series data – a data type prevalent in a multitude of disciplines. In this design study paper, we outline the design, implementation, and evaluation of "Know Your Enemy" (KYE) – a visual analytics approach to assess the quality of time series data. KYE supports the task of data profiling with (1) predefined data quality checks, (2) user-definable, customized quality checks, (3) interactive visualization to explore and reason about automatically detected problems, and (4) the visual identification of hidden quality problems.
{"title":"Know Your Enemy: Identifying Quality Problems of Time Series Data","authors":"T. Gschwandtner, Oliver Erhart","doi":"10.1109/PacificVis.2018.00034","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00034","url":null,"abstract":"Sensible data analysis requires data quality control. An essential part of this is data profiling, which is the identification and assessment of data quality problems as a prerequisite for adequately handling these problems. Differentiating between actual quality problems and unusual, but valid data values requires the \"human-in-the-loop\" through the use of visual analytics. Unfortunately, existing approaches for data profiling do not adequately support the special characteristics of time, which is imperative to identify quality problems in time series data – a data type prevalent in a multitude of disciplines. In this design study paper, we outline the design, implementation, and evaluation of \"Know Your Enemy\" (KYE) – a visual analytics approach to assess the quality of time series data. KYE supports the task of data profiling with (1) predefined data quality checks, (2) user-definable, customized quality checks, (3) interactive visualization to explore and reason about automatically detected problems, and (4) the visual identification of hidden quality problems.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"275 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120884051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00033
Chunlei Chang, Tim Dwyer, K. Marriott
We evaluate the relative merits of three techniques for visualising multivariate data: parallel coordinates; scatterplot matrix; and a side-by-side, coordinated combination of these views. In particular, we report on: (1) the most effective visual encoding of multivariate data for each of the six common tasks considered; (2) common strategies that our participants used when the two views were combined based on eye-tracking data analysis; (3) the finding that these views are perceptually complementary in the sense that they both show the same information, but with different and complementary support for different types of analysis. For the combined view, our studies show that there is a perceptually complementary effect in terms of significantly improved accuracy for certain tasks, but that there is a small cost in terms of slightly longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants were able to swiftly switch their strategies after trying both in the training phase.
{"title":"An Evaluation of Perceptually Complementary Views for Multivariate Data","authors":"Chunlei Chang, Tim Dwyer, K. Marriott","doi":"10.1109/PacificVis.2018.00033","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00033","url":null,"abstract":"We evaluate the relative merits of three techniques for visualising multivariate data: parallel coordinates; scatterplot matrix; and a side-by-side, coordinated combination of these views. In particular, we report on: (1) the most effective visual encoding of multivariate data for each of the six common tasks considered; (2) common strategies that our participants used when the two views were combined based on eye-tracking data analysis; (3) the finding that these views are perceptually complementary in the sense that they both show the same information, but with different and complementary support for different types of analysis. For the combined view, our studies show that there is a perceptually complementary effect in terms of significantly improved accuracy for certain tasks, but that there is a small cost in terms of slightly longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants were able to swiftly switch their strategies after trying both in the training phase.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00020
C. Schulz, Karsten Schatz, M. Krone, Matthias Braun, T. Ertl, D. Weiskopf
We present a technique that conveys the uncertainty in the secondary structure of proteins—an abstraction model based on atomic coordinates. While protein data inherently contains uncertainty due to the acquisition method or the simulation algorithm, we argue that it is also worth investigating uncertainty induced by analysis algorithms that precede visualization. Our technique helps researchers investigate differences between multiple secondary structure assignment methods. We modify established algorithms for fuzzy classification and introduce a discrepancy-based approach to project an ensemble of sequences to a single importance-weighted sequence. In 2D, we depict the aggregated secondary structure assignments based on the per-residue deviation in a collapsible sequence diagram. In 3D, we extend the ribbon diagram using visual variables such as transparency, wave form, frequency, or amplitude to facilitate qualitative analysis of uncertainty. We evaluated the effectiveness and acceptance of our technique through expert reviews using two example applications: the combined assignment against established algorithms and time-dependent structural changes originating from simulated protein dynamics.
{"title":"Uncertainty Visualization for Secondary Structures of Proteins","authors":"C. Schulz, Karsten Schatz, M. Krone, Matthias Braun, T. Ertl, D. Weiskopf","doi":"10.1109/PacificVis.2018.00020","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00020","url":null,"abstract":"We present a technique that conveys the uncertainty in the secondary structure of proteins—an abstraction model based on atomic coordinates. While protein data inherently contains uncertainty due to the acquisition method or the simulation algorithm, we argue that it is also worth investigating uncertainty induced by analysis algorithms that precede visualization. Our technique helps researchers investigate differences between multiple secondary structure assignment methods. We modify established algorithms for fuzzy classification and introduce a discrepancy-based approach to project an ensemble of sequences to a single importance-weighted sequence. In 2D, we depict the aggregated secondary structure assignments based on the per-residue deviation in a collapsible sequence diagram. In 3D, we extend the ribbon diagram using visual variables such as transparency, wave form, frequency, or amplitude to facilitate qualitative analysis of uncertainty. We evaluated the effectiveness and acceptance of our technique through expert reviews using two example applications: the combined assignment against established algorithms and time-dependent structural changes originating from simulated protein dynamics.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PACIFICVIS.2018.00025
David Cheng Zarate, P. L. Bodic, Tim Dwyer, G. Gange, Peter James Stuckey
We present the first practical Integer Linear Programming model for Sankey Diagram layout. We show that this approach is viable in terms of running time for reasonably complex diagrams and also that the quality of the layout is measurably and visibly better than heuristic approaches in terms of crossing reduction. Finally, we demonstrate that the model is easily extensible through the addition of constraints, such as arbitrary grouping of nodes.
{"title":"Optimal Sankey Diagrams Via Integer Programming","authors":"David Cheng Zarate, P. L. Bodic, Tim Dwyer, G. Gange, Peter James Stuckey","doi":"10.1109/PACIFICVIS.2018.00025","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2018.00025","url":null,"abstract":"We present the first practical Integer Linear Programming model for Sankey Diagram layout. We show that this approach is viable in terms of running time for reasonably complex diagrams and also that the quality of the layout is measurably and visibly better than heuristic approaches in terms of crossing reduction. Finally, we demonstrate that the model is easily extensible through the addition of constraints, such as arbitrary grouping of nodes.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121162813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00012
Yosuke Onoue, Koji Kyoda, Miki Kioka, Kazutaka Baba, Shuichi Onami, K. Koyamada
Wet and dry biological data are potentially complementary. By visually integrating the initiation and developmental processes of organisms, we might reveal new causalities in biological data. Here we present an integrated visualization system for a causality network constructed from phenotypic developmental characters and their related scientific literature. To obtain the phenotypic characters, we applied bio-imaging informatics techniques to the data of wet experiments. The phenotypic character network was visually rendered in the CausalNet system, which provides both explanatory and verification visualization functions. Statistical analysis and scientific literature mining proved useful for determining the mechanisms underlying the phenotypic trait network. The validity of the system was confirmed in an application example and expert feedback on the developmental process of the nematode Caenorhabditis elegans. The discussed methodology is applicable to other multicellular organisms.
{"title":"Development of an Integrated Visualization System for Phenotypic Character Networks","authors":"Yosuke Onoue, Koji Kyoda, Miki Kioka, Kazutaka Baba, Shuichi Onami, K. Koyamada","doi":"10.1109/PacificVis.2018.00012","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00012","url":null,"abstract":"Wet and dry biological data are potentially complementary. By visually integrating the initiation and developmental processes of organisms, we might reveal new causalities in biological data. Here we present an integrated visualization system for a causality network constructed from phenotypic developmental characters and their related scientific literature. To obtain the phenotypic characters, we applied bio-imaging informatics techniques to the data of wet experiments. The phenotypic character network was visually rendered in the CausalNet system, which provides both explanatory and verification visualization functions. Statistical analysis and scientific literature mining proved useful for determining the mechanisms underlying the phenotypic trait network. The validity of the system was confirmed in an application example and expert feedback on the developmental process of the nematode Caenorhabditis elegans. The discussed methodology is applicable to other multicellular organisms.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134579022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-04-10DOI: 10.1109/PacificVis.2018.00021
C. Gillmann, T. Wischgoll, B. Hamann, J. Ahrens
Many applications are dealing with geometric data that are affected by uncertainty. This uncertainty is important to analyze, visualize, and understand. We present a methodology to model uncertain geometry based on multi-variate normal distributions. In addition, we propose a visualization technique to represent a hull for uncertain geometry capturing a user-defined percentage of the underlying uncertain geometry. To show the effectiveness of our approach, we have modeled and visualized uncertain datasets from different applications.
{"title":"Modeling and Visualization of Uncertainty-Aware Geometry Using Multi-variate Normal Distributions","authors":"C. Gillmann, T. Wischgoll, B. Hamann, J. Ahrens","doi":"10.1109/PacificVis.2018.00021","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00021","url":null,"abstract":"Many applications are dealing with geometric data that are affected by uncertainty. This uncertainty is important to analyze, visualize, and understand. We present a methodology to model uncertain geometry based on multi-variate normal distributions. In addition, we propose a visualization technique to represent a hull for uncertain geometry capturing a user-defined percentage of the underlying uncertain geometry. To show the effectiveness of our approach, we have modeled and visualized uncertain datasets from different applications.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122463268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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