With our increasing ability to capture or produce and to store large multivariate data, these data sets are increasing both in size and complexity. Many conventional techniques for visualizing multivariate data suffer from problems like cluttered displays since they are not designed to handle these amounts of entries. We present a novel method to overcome this problem by interactively selecting and displaying statistics derived from the data in a separate view. Changes in the display are visually tracked by animation and vector plotting for easy comparison of various measures applied to different subsets of the data.
{"title":"Visual data analysis using tracked statistical measures within parallel coordinate representations","authors":"Daniel Ericson, Handledare Jimmy Johansson","doi":"10.1109/CMV.2005.21","DOIUrl":"https://doi.org/10.1109/CMV.2005.21","url":null,"abstract":"With our increasing ability to capture or produce and to store large multivariate data, these data sets are increasing both in size and complexity. Many conventional techniques for visualizing multivariate data suffer from problems like cluttered displays since they are not designed to handle these amounts of entries. We present a novel method to overcome this problem by interactively selecting and displaying statistics derived from the data in a separate view. Changes in the display are visually tracked by animation and vector plotting for easy comparison of various measures applied to different subsets of the data.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128242941","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}
It is a common task when analyzing a large dataset (e.g., census database) to create some kind of overview of the original dataset, which is small enough to be easily manipulated, while remains the key characteristics of the data. Many aggregation techniques have been proposed to help users better understand the dataset and find desired information in it. However, the user can easily get lost after several aggregation operations, since there is rarely mechanism facilitating the user to remember what he or she has done in previous steps. In this paper, we present a prototype, namely MUSA, for multiple-step aggregation visualization. We aimed at designing a tool not only to help users obtain various levels of overviews to narrow their selections, but also to effectively visualize the aggregation processes to enhance the context awareness. We also conducted an informal user study to evaluate the tool.
{"title":"MUSA - a prototype for multiple-step aggregation visualization","authors":"Tao Ni","doi":"10.1109/CMV.2005.12","DOIUrl":"https://doi.org/10.1109/CMV.2005.12","url":null,"abstract":"It is a common task when analyzing a large dataset (e.g., census database) to create some kind of overview of the original dataset, which is small enough to be easily manipulated, while remains the key characteristics of the data. Many aggregation techniques have been proposed to help users better understand the dataset and find desired information in it. However, the user can easily get lost after several aggregation operations, since there is rarely mechanism facilitating the user to remember what he or she has done in previous steps. In this paper, we present a prototype, namely MUSA, for multiple-step aggregation visualization. We aimed at designing a tool not only to help users obtain various levels of overviews to narrow their selections, but also to effectively visualize the aggregation processes to enhance the context awareness. We also conducted an informal user study to evaluate the tool.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125484428","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}
Microarray time-course data relate to the recorded activity of thousands of genes, in parallel, over multiple discrete points in time during a biological process. Existing techniques that attempt to support the exploratory analysis of this data rely on static clustering views, interactive clustering views or coordinated clustering and graph views and are limited in that they fail to account for less dominant patterns in the data such as those that involve a subset of genes or a limited interval of the time-course. In this paper, we describe an alternative approach which avoids this limitation by using combined parallel views to present different complementary aspects of the data (i.e. timing, activity and change-in-activity). An example of how the views are combined to reveal significant patterns in the data (including those which cannot be found using clustering based techniques) is described and used to illustrate the benefits of combined parallel views to support exploratory-analysis of this type of data.
{"title":"Coordinated parallel views for the exploratory analysis of microarray time-course data","authors":"Paul Craig, Jessie Kennedy, Andrew Cumming","doi":"10.1109/CMV.2005.5","DOIUrl":"https://doi.org/10.1109/CMV.2005.5","url":null,"abstract":"Microarray time-course data relate to the recorded activity of thousands of genes, in parallel, over multiple discrete points in time during a biological process. Existing techniques that attempt to support the exploratory analysis of this data rely on static clustering views, interactive clustering views or coordinated clustering and graph views and are limited in that they fail to account for less dominant patterns in the data such as those that involve a subset of genes or a limited interval of the time-course. In this paper, we describe an alternative approach which avoids this limitation by using combined parallel views to present different complementary aspects of the data (i.e. timing, activity and change-in-activity). An example of how the views are combined to reveal significant patterns in the data (including those which cannot be found using clustering based techniques) is described and used to illustrate the benefits of combined parallel views to support exploratory-analysis of this type of data.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125577276","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}
Complex simulations (in particular, those involving multiple coupled physics) cannot be understood solely using geometry-based visualizations. Such visualizations are necessary in interpreting results and gaining insights into kinematics, however they are insufficient when striving to understand why or how something happened, or when investigating a simulation's dynamic evolution. For multiphysics simulations (e.g. those including solid dynamics with thermal conduction, magnetohydrodynamics, and radiation hydrodynamics) complex interactions between physics and material properties take place within the code which must be investigated in other ways. Drawing on the extensive previous work in view coordination, brushing and linking techniques, and powerful visualization libraries, we have developed Prism, an application targeted for a specific analytic need at Sandia National Laboratories. This multiview scientific visualization tool tightly integrates geometric and phase space views of simulation data and material models. Working closely with analysts, we have developed this production tool to promote understanding of complex, multiphysics simulations. We discuss the current implementation of Prism, along with specific examples of results obtained by using the tool.
{"title":"Prism: a multi-view visualization tool for multi-physics simulation","authors":"D. Rogers, C. Garasi","doi":"10.1109/CMV.2005.15","DOIUrl":"https://doi.org/10.1109/CMV.2005.15","url":null,"abstract":"Complex simulations (in particular, those involving multiple coupled physics) cannot be understood solely using geometry-based visualizations. Such visualizations are necessary in interpreting results and gaining insights into kinematics, however they are insufficient when striving to understand why or how something happened, or when investigating a simulation's dynamic evolution. For multiphysics simulations (e.g. those including solid dynamics with thermal conduction, magnetohydrodynamics, and radiation hydrodynamics) complex interactions between physics and material properties take place within the code which must be investigated in other ways. Drawing on the extensive previous work in view coordination, brushing and linking techniques, and powerful visualization libraries, we have developed Prism, an application targeted for a specific analytic need at Sandia National Laboratories. This multiview scientific visualization tool tightly integrates geometric and phase space views of simulation data and material models. Working closely with analysts, we have developed this production tool to promote understanding of complex, multiphysics simulations. We discuss the current implementation of Prism, along with specific examples of results obtained by using the tool.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133639006","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}
In this paper we integrate radviz and parallel coordinates, two methods able to handle multidimensional datasets, exploiting their contrasting characteristics. From on side radviz offers good direct data manipulation (i.e., brushing) techniques and low cluttering but it fails in providing visualization of quantitative information; conversely, parallel coordinates clearly shows the values of data attributes and their ranges but suffers from high cluttering also on small datasets and presents tedious manipulation techniques. We developed a prototype, called SpringView, that allows for simultaneously viewing both radviz and parallel coordinates and implements several useful techniques to manipulate the data, both interactively and, more interestingly, automatically. We challenged our approach against two well know multidimensional datasets, proving its effectiveness.
{"title":"SpringView: cooperation of radviz and parallel coordinates for view optimization and clutter reduction","authors":"E. Bertini, L. Dell'Aquila, G. Santucci","doi":"10.1109/CMV.2005.17","DOIUrl":"https://doi.org/10.1109/CMV.2005.17","url":null,"abstract":"In this paper we integrate radviz and parallel coordinates, two methods able to handle multidimensional datasets, exploiting their contrasting characteristics. From on side radviz offers good direct data manipulation (i.e., brushing) techniques and low cluttering but it fails in providing visualization of quantitative information; conversely, parallel coordinates clearly shows the values of data attributes and their ranges but suffers from high cluttering also on small datasets and presents tedious manipulation techniques. We developed a prototype, called SpringView, that allows for simultaneously viewing both radviz and parallel coordinates and implements several useful techniques to manipulate the data, both interactively and, more interestingly, automatically. We challenged our approach against two well know multidimensional datasets, proving its effectiveness.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"66 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933084","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}
In this paper, we present linked 2D/3D texture advection for the interactive exploration of 3D flow. 3D texture advection facilitates a dense representation of the 3D structure of unsteady flow but is subject to problems of occlusion and clutter. Therefore, it is difficult for the user to explore features in occluded regions. We overcome the occlusion problem by adopting an additional 2D representation on several parallel slices through the data set. By linking these two views, our approach allows the user to gain unrestricted access to all spatial areas of the data set and, at the same time, retain a view on the 3D nature of the flow. Furthermore, the 2D view is used to visualize an additional attribute of the data set by color coding, such as vortex strength, temperature, or velocity magnitude. The 2D view lets the user explore flow features by selecting interesting values in this attribute space. A brushing and linking mechanism provides immediate feedback by highlighting selected data values in both the 2D and 3D representations. Finally, we discuss a GPU implementation of our visualization approach that is the technical basis for interactive exploration and real-time visualization without the need for preprocessing.
{"title":"Interactive exploration of unsteady 3D flow with linked 2D/3D texture advection","authors":"T. Schafhitzel, D. Weiskopf, T. Ertl","doi":"10.1109/CMV.2005.9","DOIUrl":"https://doi.org/10.1109/CMV.2005.9","url":null,"abstract":"In this paper, we present linked 2D/3D texture advection for the interactive exploration of 3D flow. 3D texture advection facilitates a dense representation of the 3D structure of unsteady flow but is subject to problems of occlusion and clutter. Therefore, it is difficult for the user to explore features in occluded regions. We overcome the occlusion problem by adopting an additional 2D representation on several parallel slices through the data set. By linking these two views, our approach allows the user to gain unrestricted access to all spatial areas of the data set and, at the same time, retain a view on the 3D nature of the flow. Furthermore, the 2D view is used to visualize an additional attribute of the data set by color coding, such as vortex strength, temperature, or velocity magnitude. The 2D view lets the user explore flow features by selecting interesting values in this attribute space. A brushing and linking mechanism provides immediate feedback by highlighting selected data values in both the 2D and 3D representations. Finally, we discuss a GPU implementation of our visualization approach that is the technical basis for interactive exploration and real-time visualization without the need for preprocessing.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122518697","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}
Large information or model structures often span many scale levels, and exhibit important features at each scale. Having a coherent and cross-scale understanding of such multiscale structures often requires users to interact with the structures at different scales. While multiple views allow users to see the structures from different locations and at different scales, establishing cross-scale connections between structures in divergent multiple views could be a challenge. This paper proposes a new interactive design, space-scale animation, to visualize the spatial and semantic relationship between structures in different views, and discusses the design and implementation of space-scale animation as a dynamic view transition traversing space and scale. This research not only extends interactive animation techniques by explicitly considering the scale factor, but also argues the necessity to integrate cross-scale semantic information into animation to improve the understanding of complex structures.
{"title":"Space-scale animation: enhancing cross-scale understanding of multiscale structures in multiple views","authors":"Xiaolong Zhang","doi":"10.1109/CMV.2005.16","DOIUrl":"https://doi.org/10.1109/CMV.2005.16","url":null,"abstract":"Large information or model structures often span many scale levels, and exhibit important features at each scale. Having a coherent and cross-scale understanding of such multiscale structures often requires users to interact with the structures at different scales. While multiple views allow users to see the structures from different locations and at different scales, establishing cross-scale connections between structures in divergent multiple views could be a challenge. This paper proposes a new interactive design, space-scale animation, to visualize the spatial and semantic relationship between structures in different views, and discusses the design and implementation of space-scale animation as a dynamic view transition traversing space and scale. This research not only extends interactive animation techniques by explicitly considering the scale factor, but also argues the necessity to integrate cross-scale semantic information into animation to improve the understanding of complex structures.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125624904","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}
Unforeseen change propagation can have a major impact on products and design processes and cause project delays and excessive costs. However, current change management depends heavily on individual designers' typically limited product overview. For complex products, this approach is error-prone because the amount of data that is necessary to properly assess the risk of changes is too large. The information has to be broken down into smaller chunks so that it is easier to cope with. On the other hand, an overview over the entire product must be provided in order to be able to predict changes resulting from changes in other components. In this paper, we discuss the CPM (change prediction method) tool that incorporates a multiple view strategy to visualise complex change data and allows designers to run what-if scenarios in order to assess the implications of changing components in a complex product during the design process.
{"title":"Multiple views to support engineering change management for complex products","authors":"R. Keller, C. Eckert, P. Clarkson","doi":"10.1109/CMV.2005.11","DOIUrl":"https://doi.org/10.1109/CMV.2005.11","url":null,"abstract":"Unforeseen change propagation can have a major impact on products and design processes and cause project delays and excessive costs. However, current change management depends heavily on individual designers' typically limited product overview. For complex products, this approach is error-prone because the amount of data that is necessary to properly assess the risk of changes is too large. The information has to be broken down into smaller chunks so that it is easier to cope with. On the other hand, an overview over the entire product must be provided in order to be able to predict changes resulting from changes in other components. In this paper, we discuss the CPM (change prediction method) tool that incorporates a multiple view strategy to visualise complex change data and allows designers to run what-if scenarios in order to assess the implications of changing components in a complex product during the design process.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131572273","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}
Sweden's statistical databases are maintained by Statistics Sweden and can be accessed free of charge via the Web. We provide an easy-to-use, exploratory visualization application, called "GeoWizard" that lets users of these databases explore data, construct hypotheses, discover, refine, test knowledge and evaluate results. Our target user group is not restricted to experts, but we want a broader group of analysts to feel comfortable with our human interaction tools. The voluminous high-dimensional nature of the statistical databases calls for high interactive performance and creative integrated information visualization and geovisualization methods. Tailor-made and Web-enabled applications based on layered component thinking are the foundation for our research. We present a development platform approach that, instead of Java, uses Microsoft's .NET framework, which can integrate a wide range of problem-solving components, both computationally and visually. The approach facilitates .NET hierarchical layout management for implementation of dynamic and resizable views in a single coherent GUI window and the Open-Viz data model optimized for efficiency and interactivity in handling large multivariate data sets. We introduce a parallel coordinates browser (PCB) that serves as the control panel for easier identification of multivariate relationships across spatial domains in the choropleth map. The PCB integrates range sliders for both dynamic queries and conditioning that constrains the data displayed to those meeting specified parameters on all attributes in the PCB. Finally, we present a client-side, plug-in architecture that enables a light-weight GeoWizard application to be distributed across the Web to the users of the statistical databases.
{"title":"Tailor-made exploratory visualization for statistics Sweden","authors":"N. Feldt, H. Pettersson, J. Johansson, M. Jern","doi":"10.1109/CMV.2005.19","DOIUrl":"https://doi.org/10.1109/CMV.2005.19","url":null,"abstract":"Sweden's statistical databases are maintained by Statistics Sweden and can be accessed free of charge via the Web. We provide an easy-to-use, exploratory visualization application, called \"GeoWizard\" that lets users of these databases explore data, construct hypotheses, discover, refine, test knowledge and evaluate results. Our target user group is not restricted to experts, but we want a broader group of analysts to feel comfortable with our human interaction tools. The voluminous high-dimensional nature of the statistical databases calls for high interactive performance and creative integrated information visualization and geovisualization methods. Tailor-made and Web-enabled applications based on layered component thinking are the foundation for our research. We present a development platform approach that, instead of Java, uses Microsoft's .NET framework, which can integrate a wide range of problem-solving components, both computationally and visually. The approach facilitates .NET hierarchical layout management for implementation of dynamic and resizable views in a single coherent GUI window and the Open-Viz data model optimized for efficiency and interactivity in handling large multivariate data sets. We introduce a parallel coordinates browser (PCB) that serves as the control panel for easier identification of multivariate relationships across spatial domains in the choropleth map. The PCB integrates range sliders for both dynamic queries and conditioning that constrains the data displayed to those meeting specified parameters on all attributes in the PCB. Finally, we present a client-side, plug-in architecture that enables a light-weight GeoWizard application to be distributed across the Web to the users of the statistical databases.","PeriodicalId":153029,"journal":{"name":"Coordinated and Multiple Views in Exploratory Visualization (CMV'05)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123178646","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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