IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization最新文献
Jens Krüger, Kristin Potter, Rob S Macleod, Christopher Johnson
With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.
{"title":"Uvf - Unified Volume Format: A General System for Efficient Handling of Large Volumetric Datasets.","authors":"Jens Krüger, Kristin Potter, Rob S Macleod, Christopher Johnson","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.</p>","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"2008 ","pages":"19-26"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2954506/pdf/nihms146727.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29358469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2004-01-01DOI: 10.1057/palgrave.ivs.9500073
S. North, T. Munzner
{"title":"Special Issue of selected and extended InfoVis '03 papers - Guest Editor' Introduction","authors":"S. North, T. Munzner","doi":"10.1057/palgrave.ivs.9500073","DOIUrl":"https://doi.org/10.1057/palgrave.ivs.9500073","url":null,"abstract":"","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"37 1","pages":"63-64"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84898432","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 : 2003-01-01DOI: 10.1109/INFVIS.2003.1249006
Alan Maceachren, Xiping Dai, Frank Hardisty, Diansheng Guo, Gene Lengerich
We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.
{"title":"Exploring High-D Spaces with Multiform Matrices and Small Multiples.","authors":"Alan Maceachren, Xiping Dai, Frank Hardisty, Diansheng Guo, Gene Lengerich","doi":"10.1109/INFVIS.2003.1249006","DOIUrl":"https://doi.org/10.1109/INFVIS.2003.1249006","url":null,"abstract":"<p><p>We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.</p>","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":" ","pages":"31-38"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/INFVIS.2003.1249006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30165186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-10-28DOI: 10.1057/palgrave.ivs.9500036
F. V. Ham, J. V. Wijk
{"title":"Beamtrees: compact visualization of large hierarchies","authors":"F. V. Ham, J. V. Wijk","doi":"10.1057/palgrave.ivs.9500036","DOIUrl":"https://doi.org/10.1057/palgrave.ivs.9500036","url":null,"abstract":"","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"133 1","pages":"31-39"},"PeriodicalIF":0.0,"publicationDate":"2002-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78345733","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 : 2002-03-01DOI: 10.1057/palgrave/ivs/9500003
D. Keim, M. Hao, U. Dayal, M. Hsu
{"title":"Pixel bar charts: a visualization technique for very large multi-attribute data sets?","authors":"D. Keim, M. Hao, U. Dayal, M. Hsu","doi":"10.1057/palgrave/ivs/9500003","DOIUrl":"https://doi.org/10.1057/palgrave/ivs/9500003","url":null,"abstract":"","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"31 1","pages":"20-34"},"PeriodicalIF":0.0,"publicationDate":"2002-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73654491","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 : 2000-10-09DOI: 10.1109/INFOVIS.2000.10000
J. Mackinlay
{"title":"Presentation, Visualization, What's Next?","authors":"J. Mackinlay","doi":"10.1109/INFOVIS.2000.10000","DOIUrl":"https://doi.org/10.1109/INFOVIS.2000.10000","url":null,"abstract":"","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"17 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2000-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81987852","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 : 1995-10-30DOI: 10.1109/INFOVIS.1995.10002
S. Card
A control valve assembly incorporates direct metal-to-metal engagement between a casing of a temperature-responsive valve actuator and a piston guide to promote ease and accuracy of calibration. In addition, a substantially frictionless override feature is incorporated in the assembly to effect pressure relief and to accommodate thermal excursion of the valve actuator beyond the travel required to effect the desired valve movement.
{"title":"Keynote Address: Information Visualization: Wings for the Mind","authors":"S. Card","doi":"10.1109/INFOVIS.1995.10002","DOIUrl":"https://doi.org/10.1109/INFOVIS.1995.10002","url":null,"abstract":"A control valve assembly incorporates direct metal-to-metal engagement between a casing of a temperature-responsive valve actuator and a piston guide to promote ease and accuracy of calibration. In addition, a substantially frictionless override feature is incorporated in the assembly to effect pressure relief and to accommodate thermal excursion of the valve actuator beyond the travel required to effect the desired valve movement.","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"1 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"1995-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78539622","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 : 1995-01-01DOI: 10.1109/INFVIS.1995.528691
R. Hendley, N. Drew, Andrew Wood, R. Beale
{"title":"Case study: Narcissus: visualising information","authors":"R. Hendley, N. Drew, Andrew Wood, R. Beale","doi":"10.1109/INFVIS.1995.528691","DOIUrl":"https://doi.org/10.1109/INFVIS.1995.528691","url":null,"abstract":"","PeriodicalId":88889,"journal":{"name":"IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization","volume":"16 1","pages":"90-96"},"PeriodicalIF":0.0,"publicationDate":"1995-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82289181","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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