Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742383
O. Hoeber, G. Wilson, Simon Harding, René Enguehard, R. Devillers
Many data sets exist that contain both geospatial and temporal elements, in addition to the core data that requires analysis. Within such data sets, it can be difficult to determine how the data have changed over spatial and temporal ranges. In this design study we present a system for dynamically exploring geo-temporal changes in the data. GTdiff provides a visual approach to representing differences in the data within user-defined spatial and temporal limits, illustrating when and where increases and/or decreases have occurred. The system makes extensive use of spatial and temporal filtering and binning, geo-visualization, colour encoding, and multiple coordinated views. It is highly interactive, supporting knowledge discovery through exploration and analysis of the data. A case study is presented illustrating the benefits of using GTdiff to analyze the changes in the catch data of the cod fisheries off the coast of Newfoundland, Canada from 1948 to 2006.
{"title":"Exploring geo-temporal differences using GTdiff","authors":"O. Hoeber, G. Wilson, Simon Harding, René Enguehard, R. Devillers","doi":"10.1109/PACIFICVIS.2011.5742383","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742383","url":null,"abstract":"Many data sets exist that contain both geospatial and temporal elements, in addition to the core data that requires analysis. Within such data sets, it can be difficult to determine how the data have changed over spatial and temporal ranges. In this design study we present a system for dynamically exploring geo-temporal changes in the data. GTdiff provides a visual approach to representing differences in the data within user-defined spatial and temporal limits, illustrating when and where increases and/or decreases have occurred. The system makes extensive use of spatial and temporal filtering and binning, geo-visualization, colour encoding, and multiple coordinated views. It is highly interactive, supporting knowledge discovery through exploration and analysis of the data. A case study is presented illustrating the benefits of using GTdiff to analyze the changes in the catch data of the cod fisheries off the coast of Newfoundland, Canada from 1948 to 2006.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134640845","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742369
Cheng-Kai Chen, Chaoli Wang, K. Ma, A. Wittenberg
Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.
{"title":"Static correlation visualization for large time-varying volume data","authors":"Cheng-Kai Chen, Chaoli Wang, K. Ma, A. Wittenberg","doi":"10.1109/PACIFICVIS.2011.5742369","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742369","url":null,"abstract":"Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117089768","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742375
H. Bhatia, Shreeraj Jadhav, P. Bremer, Guoning Chen, J. Levine, L. G. Nonato, Valerio Pascucci
Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result [15] shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques.
{"title":"Edge maps: Representing flow with bounded error","authors":"H. Bhatia, Shreeraj Jadhav, P. Bremer, Guoning Chen, J. Levine, L. G. Nonato, Valerio Pascucci","doi":"10.1109/PACIFICVIS.2011.5742375","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742375","url":null,"abstract":"Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result [15] shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129511508","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742364
A. Kaufman
Scientists, engineers and physicians are now confronted with a fire hose of data. Immersive visualization environments provide these users with a novel way of interacting and reasoning with large datasets. They allow them to utilize the entirety of their visual bandwidth, effectively engulfing the user in the data and enabling collaborative interaction. We present a custom-built 5-wall Cave environment, called the Immersive Cabin (IC). It is driven by a GPU cluster for both computation and 3D stereo rendering. We also propose a conformal deformation rendering pipeline for the visualization of datasets on partially-immersive platforms. Combined with a range of interaction and navigation tools, our system can support numerous interactive applications of large datasets. Several demonstrations include architectural visualization, urban planning, medical visualization, simulation and rendering of physical phenomena, and entertainment. Current visualization displays, however, have not kept up with the explosive growth in data size and resolution, which is beginning to match the resolution of the visuals that surround us in daily life. To ameliorate this challenge, we have developed a life-like, realistic immersion into the petascale data to be explored, appropriately called The RealityDeck. It is a one-of-a kind pioneering G-pixel immersive and collaborative display system - a unique assembly of high-res display panels, GPU cluster, sensors, networking, computer vision, and human-computer interaction technologies.
{"title":"Keynote address: Immersive exploration of large datasets","authors":"A. Kaufman","doi":"10.1109/PACIFICVIS.2011.5742364","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742364","url":null,"abstract":"Scientists, engineers and physicians are now confronted with a fire hose of data. Immersive visualization environments provide these users with a novel way of interacting and reasoning with large datasets. They allow them to utilize the entirety of their visual bandwidth, effectively engulfing the user in the data and enabling collaborative interaction. We present a custom-built 5-wall Cave environment, called the Immersive Cabin (IC). It is driven by a GPU cluster for both computation and 3D stereo rendering. We also propose a conformal deformation rendering pipeline for the visualization of datasets on partially-immersive platforms. Combined with a range of interaction and navigation tools, our system can support numerous interactive applications of large datasets. Several demonstrations include architectural visualization, urban planning, medical visualization, simulation and rendering of physical phenomena, and entertainment. Current visualization displays, however, have not kept up with the explosive growth in data size and resolution, which is beginning to match the resolution of the visuals that surround us in daily life. To ameliorate this challenge, we have developed a life-like, realistic immersion into the petascale data to be explored, appropriately called The RealityDeck. It is a one-of-a kind pioneering G-pixel immersive and collaborative display system - a unique assembly of high-res display panels, GPU cluster, sensors, networking, computer vision, and human-computer interaction technologies.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128952248","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742379
Taylor Sando, Melanie Tory, Pourang Irani
Co-located collaborative tasks allow teams to leverage the skills of each individual member. While numerous guidelines exist to develop visualizations for individuals working on desktops, very little is known about how groups of individuals interpret and comprehend diverse types of visual constructs on larger displays. To study whether group size impacts the collective understanding of relationships in three-dimensional (3D) spatial structures when using different types of presentation, we carried out three experiments. We compared individual performance at structure understanding tasks to performance of groups containing two or four members. We consider two alternate visualization techniques for extracting 3D structure information: a 3D view with animated rotations and a combination of one static 3D plus three static two-dimensional (2D) projection views. In general our studies suggest that as group size increases, so does accuracy but with a cost in efficiency. Our results also suggest that beyond a threshold limit in group size, performance on certain tasks begins to degrade. Regardless of group size, participants performed better when the display was presented in the animation condition instead of the multiple static views, except when large groups needed to relate the visualization to a physical counterpart. We summarize our results in terms of Steiner's model for explaining the effects of group size and task characteristics on group performance.
{"title":"Impact of group size on spatial structure understanding tasks","authors":"Taylor Sando, Melanie Tory, Pourang Irani","doi":"10.1109/PACIFICVIS.2011.5742379","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742379","url":null,"abstract":"Co-located collaborative tasks allow teams to leverage the skills of each individual member. While numerous guidelines exist to develop visualizations for individuals working on desktops, very little is known about how groups of individuals interpret and comprehend diverse types of visual constructs on larger displays. To study whether group size impacts the collective understanding of relationships in three-dimensional (3D) spatial structures when using different types of presentation, we carried out three experiments. We compared individual performance at structure understanding tasks to performance of groups containing two or four members. We consider two alternate visualization techniques for extracting 3D structure information: a 3D view with animated rotations and a combination of one static 3D plus three static two-dimensional (2D) projection views. In general our studies suggest that as group size increases, so does accuracy but with a cost in efficiency. Our results also suggest that beyond a threshold limit in group size, performance on certain tasks begins to degrade. Regardless of group size, participants performed better when the display was presented in the animation condition instead of the multiple static views, except when large groups needed to relate the visualization to a physical counterpart. We summarize our results in terms of Steiner's model for explaining the effects of group size and task characteristics on group performance.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130521058","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742372
S. Frey, T. Schlömer, Sebastian Grottel, C. Dachsbacher, O. Deussen, T. Ertl
Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles and thousands of time steps. Some application domains have developed special visual metaphors to only represent the relevant information of such data sets but these approaches typically require detailed domain knowledge that might not always be available or applicable. We propose a general technique that replaces the huge amount of simulated particles by a smaller set of representatives that are used for the visualization instead. The representatives capture the characteristics of the underlying particle density and exhibit coherency over time. We introduce loose capacity-constrained Voronoi diagrams for the generation of these representatives by means of a GPU-friendly, parallel algorithm. This way we achieve visualizations that reflect the particle distribution and geometric structure of the original data very faithfully. We evaluate our approach using real-world data sets from the application domains of material science, thermodynamics and dynamical systems theory.
{"title":"Loose capacity-constrained representatives for the qualitative visual analysis in molecular dynamics","authors":"S. Frey, T. Schlömer, Sebastian Grottel, C. Dachsbacher, O. Deussen, T. Ertl","doi":"10.1109/PACIFICVIS.2011.5742372","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742372","url":null,"abstract":"Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles and thousands of time steps. Some application domains have developed special visual metaphors to only represent the relevant information of such data sets but these approaches typically require detailed domain knowledge that might not always be available or applicable. We propose a general technique that replaces the huge amount of simulated particles by a smaller set of representatives that are used for the visualization instead. The representatives capture the characteristics of the underlying particle density and exhibit coherency over time. We introduce loose capacity-constrained Voronoi diagrams for the generation of these representatives by means of a GPU-friendly, parallel algorithm. This way we achieve visualizations that reflect the particle distribution and geometric structure of the original data very faithfully. We evaluate our approach using real-world data sets from the application domains of material science, thermodynamics and dynamical systems theory.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130696563","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742368
Hanqi Guo, He Xiao, Xiaoru Yuan
In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration.
{"title":"Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates","authors":"Hanqi Guo, He Xiao, Xiaoru Yuan","doi":"10.1109/PACIFICVIS.2011.5742368","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742368","url":null,"abstract":"In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128282659","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742382
J. Alsakran, Yang Chen, Ye Zhao, Jing Yang, Dongning Luo
Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.
{"title":"STREAMIT: Dynamic visualization and interactive exploration of text streams","authors":"J. Alsakran, Yang Chen, Ye Zhao, Jing Yang, Dongning Luo","doi":"10.1109/PACIFICVIS.2011.5742382","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742382","url":null,"abstract":"Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132291053","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 : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742365
K. Ma
Advanced computing and imaging technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. Furthermore, the size of the collected information about the Internet and mobile device users is expected to be even greater, a daunting challenge we must address in order to make sense and maximize utilization of all the available information for decision making and knowledge discovery. I will introduce a few new approaches to large data visualization for revealing hidden structures and gleaning insights from large, complex data found in many areas of study.
{"title":"Keynote address: New approaches to large data visualization","authors":"K. Ma","doi":"10.1109/PACIFICVIS.2011.5742365","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742365","url":null,"abstract":"Advanced computing and imaging technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. Furthermore, the size of the collected information about the Internet and mobile device users is expected to be even greater, a daunting challenge we must address in order to make sense and maximize utilization of all the available information for decision making and knowledge discovery. I will introduce a few new approaches to large data visualization for revealing hidden structures and gleaning insights from large, complex data found in many areas of study.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184144","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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