Pub Date : 2013-09-12DOI: 10.1109/PACIFICVIS.2013.6596122
Meng-Wei Chang, C. Collins
We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization. The visualization encodes text data onto 3D models, emphasizing the relative significance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models. We applied our method to analyze a collection of vehicle complaint reports from the National Highway Traffic Safety Administration (NHTSA), and through a qualitative study, we demonstrate how our system can support tasks such as comparing the reliability of different models, finding interesting facts, and revealing possible causal relations between car parts.
{"title":"Exploring entities in text with descriptive non-photorealistic rendering","authors":"Meng-Wei Chang, C. Collins","doi":"10.1109/PACIFICVIS.2013.6596122","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2013.6596122","url":null,"abstract":"We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization. The visualization encodes text data onto 3D models, emphasizing the relative significance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models. We applied our method to analyze a collection of vehicle complaint reports from the National Highway Traffic Safety Administration (NHTSA), and through a qualitative study, we demonstrate how our system can support tasks such as comparing the reliability of different models, finding interesting facts, and revealing possible causal relations between car parts.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126525793","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596153
Kewei Lu, Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, P. C. Wong
Streamline-based techniques are designed based on the idea that properties of streamlines are indicative of features in the underlying field. In this paper, we show that statistical distributions of measurements along the trajectory of a streamline can be used as a robust and effective descriptor to measure the similarity between streamlines. With the distribution-based approach, we present a framework for interactive exploration of 3D vector fields with streamline query and clustering. Streamline queries allow us to rapidly identify streamlines that share similar geometric features to the target streamline. Streamline clustering allows us to group together streamlines of similar shapes. Based on user's selection, different clusters with different features at different levels of detail can be visualized to highlight features in 3D flow fields. We demonstrate the utility of our framework with simulation data sets of varying nature and size.
{"title":"Exploring vector fields with distribution-based streamline analysis","authors":"Kewei Lu, Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, P. C. Wong","doi":"10.1109/PacificVis.2013.6596153","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596153","url":null,"abstract":"Streamline-based techniques are designed based on the idea that properties of streamlines are indicative of features in the underlying field. In this paper, we show that statistical distributions of measurements along the trajectory of a streamline can be used as a robust and effective descriptor to measure the similarity between streamlines. With the distribution-based approach, we present a framework for interactive exploration of 3D vector fields with streamline query and clustering. Streamline queries allow us to rapidly identify streamlines that share similar geometric features to the target streamline. Streamline clustering allows us to group together streamlines of similar shapes. Based on user's selection, different clusters with different features at different levels of detail can be visualized to highlight features in 3D flow fields. We demonstrate the utility of our framework with simulation data sets of varying nature and size.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127819001","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596136
G. Karch, F. Sadlo, C. Meister, Philipp Rauschenberger, Kathrin Eisenschmidt, B. Weigand, T. Ertl
Piecewise linear interface calculation (PLIC) is one of the most widely employed reconstruction schemes for the simulation of multiphase flow. In this visualization paper we focus on the reconstruction from the simulation point of view, i.e., we present a framework for the analysis of this reconstruction scheme together with its implications on the overall simulation. By interpreting PLIC reconstruction as an isosurface extraction problem from the first-order Taylor approximation of the underlying volume of fluid field, we obtain a framework for error analysis and geometric representation of the reconstruction including the fluxes involved in the simulation. At the same time this generalizes PLIC to higher-order approximation. We exemplify the utility and versatility of our visualization approach on several multiphase CFD examples.
{"title":"Visualization of piecewise linear interface calculation","authors":"G. Karch, F. Sadlo, C. Meister, Philipp Rauschenberger, Kathrin Eisenschmidt, B. Weigand, T. Ertl","doi":"10.1109/PacificVis.2013.6596136","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596136","url":null,"abstract":"Piecewise linear interface calculation (PLIC) is one of the most widely employed reconstruction schemes for the simulation of multiphase flow. In this visualization paper we focus on the reconstruction from the simulation point of view, i.e., we present a framework for the analysis of this reconstruction scheme together with its implications on the overall simulation. By interpreting PLIC reconstruction as an isosurface extraction problem from the first-order Taylor approximation of the underlying volume of fluid field, we obtain a framework for error analysis and geometric representation of the reconstruction including the fluxes involved in the simulation. At the same time this generalizes PLIC to higher-order approximation. We exemplify the utility and versatility of our visualization approach on several multiphase CFD examples.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121941205","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 : 2013-09-12DOI: 10.1109/PACIFICVIS.2013.6596128
D. Koop, J. Freire, Cláudio T. Silva
Graphs can be used to represent a variety of information, from molecular structures to biological pathways to computational workflows. With a growing volume of data represented as graphs, the problem of understanding and analyzing the variations in a collection of graphs is of increasing importance. We present an algorithm to compute a single summary graph that efficiently encodes an entire collection of graphs by finding and merging similar nodes and edges. Instead of only merging nodes and edges that are exactly the same, we use domain-specific comparison functions to collapse similar nodes and edges which allows us to generate more compact representations of the collection. In addition, we have developed methods that allow users to interactively control the display of these summary graphs. These interactions include the ability to highlight individual graphs in the summary, control the succinctness of the summary, and explicitly define when specific nodes should or should not be merged. We show that our approach to generating and interacting with graph summaries leads to a better understanding of a graph collection by allowing users to more easily identify common substructures and key differences between graphs.
{"title":"Visual summaries for graph collections","authors":"D. Koop, J. Freire, Cláudio T. Silva","doi":"10.1109/PACIFICVIS.2013.6596128","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2013.6596128","url":null,"abstract":"Graphs can be used to represent a variety of information, from molecular structures to biological pathways to computational workflows. With a growing volume of data represented as graphs, the problem of understanding and analyzing the variations in a collection of graphs is of increasing importance. We present an algorithm to compute a single summary graph that efficiently encodes an entire collection of graphs by finding and merging similar nodes and edges. Instead of only merging nodes and edges that are exactly the same, we use domain-specific comparison functions to collapse similar nodes and edges which allows us to generate more compact representations of the collection. In addition, we have developed methods that allow users to interactively control the display of these summary graphs. These interactions include the ability to highlight individual graphs in the summary, control the succinctness of the summary, and explicitly define when specific nodes should or should not be merged. We show that our approach to generating and interacting with graph summaries leads to a better understanding of a graph collection by allowing users to more easily identify common substructures and key differences between graphs.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123076615","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596143
R. J. Crouser, Jeremy G. Freeman, Andrew Winslow, Remco Chang
Agent-based simulation has become a key technique for modeling and simulating dynamic, complicated behaviors in social and behavioral sciences. As these simulations become more complex, they generate an increasingly large amount of data. Lacking the appropriate tools and support, it has become difficult for social scientists to interpret and analyze the results of these simulations. In this paper, we introduce the Aggregate Temporal Graph (ATG), a graph formulation that can be used to capture complex relationships between discrete simulation states in time. Using this formulation, we can assist social scientists in identifying critical simulation states by examining graph substructures. In particular, we define the concept of a Gateway and its inverse, a Terminal, which capture the relationships between pivotal states in the simulation and their inevitable outcomes. We propose two real-time computable algorithms to identify these relationships and provide a proof of correctness, complexity analysis, and empirical run-time analysis. We demonstrate the use of these algorithms on a large-scale social science simulation of political power and violence in present-day Thailand, and discuss broader applications of the ATG and associated algorithms in other domains such as analytic provenance.
{"title":"Exploring agent-based simulations in political science using Aggregate Temporal Graphs","authors":"R. J. Crouser, Jeremy G. Freeman, Andrew Winslow, Remco Chang","doi":"10.1109/PacificVis.2013.6596143","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596143","url":null,"abstract":"Agent-based simulation has become a key technique for modeling and simulating dynamic, complicated behaviors in social and behavioral sciences. As these simulations become more complex, they generate an increasingly large amount of data. Lacking the appropriate tools and support, it has become difficult for social scientists to interpret and analyze the results of these simulations. In this paper, we introduce the Aggregate Temporal Graph (ATG), a graph formulation that can be used to capture complex relationships between discrete simulation states in time. Using this formulation, we can assist social scientists in identifying critical simulation states by examining graph substructures. In particular, we define the concept of a Gateway and its inverse, a Terminal, which capture the relationships between pivotal states in the simulation and their inevitable outcomes. We propose two real-time computable algorithms to identify these relationships and provide a proof of correctness, complexity analysis, and empirical run-time analysis. We demonstrate the use of these algorithms on a large-scale social science simulation of political power and violence in present-day Thailand, and discuss broader applications of the ATG and associated algorithms in other domains such as analytic provenance.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932666","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596150
Jun Ma, Chaoli Wang, Ching-Kuang Shene
Visual exploration of large and complex 3D flow fields is critically important for understanding many aero- and hydro-dynamical systems that dominate various physical and natural phenomena in the world. In this paper, we introduce the FlowGraph, a novel compound graph representation that organizes streamline clusters and spatial regions hierarchically for occlusion-free and controllable visual exploration. Our approach works with any seeding strategies as long as the domain is well covered and important flow features are captured. By transforming a flow field to a graph representation, we enable observation and exploration of the relationships among streamline clusters, spatial regions and their interconnection in the transformed space. The FlowGraph not only provides a visual mapping that abstracts streamline clusters and spatial regions in various levels of detail, but also serves as a navigation tool that guides flow field exploration and understanding. Through brushing and linking in conjunction with the spatial streamline view, we demonstrate the effectiveness of FlowGraph with several visual exploration and comparison tasks that can not be well accomplished using the streamline view alone. As occlusion and clutter are almost ubiquitous in 3D flows, the FlowGraph represents a promising direction for enhancing our ability to understand large and complex flow field data.
{"title":"FlowGraph: A compound hierarchical graph for flow field exploration","authors":"Jun Ma, Chaoli Wang, Ching-Kuang Shene","doi":"10.1109/PacificVis.2013.6596150","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596150","url":null,"abstract":"Visual exploration of large and complex 3D flow fields is critically important for understanding many aero- and hydro-dynamical systems that dominate various physical and natural phenomena in the world. In this paper, we introduce the FlowGraph, a novel compound graph representation that organizes streamline clusters and spatial regions hierarchically for occlusion-free and controllable visual exploration. Our approach works with any seeding strategies as long as the domain is well covered and important flow features are captured. By transforming a flow field to a graph representation, we enable observation and exploration of the relationships among streamline clusters, spatial regions and their interconnection in the transformed space. The FlowGraph not only provides a visual mapping that abstracts streamline clusters and spatial regions in various levels of detail, but also serves as a navigation tool that guides flow field exploration and understanding. Through brushing and linking in conjunction with the spatial streamline view, we demonstrate the effectiveness of FlowGraph with several visual exploration and comparison tasks that can not be well accomplished using the streamline view alone. As occlusion and clutter are almost ubiquitous in 3D flows, the FlowGraph represents a promising direction for enhancing our ability to understand large and complex flow field data.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113952228","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596139
Jingyuan Wang, R. Sisneros, Jian Huang
Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features-concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses.
{"title":"Interactive selection of multivariate features in large spatiotemporal data","authors":"Jingyuan Wang, R. Sisneros, Jian Huang","doi":"10.1109/PacificVis.2013.6596139","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596139","url":null,"abstract":"Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features-concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134270021","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596154
Cornelia Auer, Jens Kasten, A. Kratz, E. Zhang, I. Hotz
This paper proposes a vector field visualization, which mimics a sketch-like representation. The visualization combines two major perspectives: Large scale trends based on a strongly simplified field as background visualization and a local visualization highlighting strongly expressed features at their exact position. Each component considers the vector field itself and its spatial derivatives. The derivate is an asymmetric tensor field, which allows the deduction of scalar quantities reflecting distinctive field properties like strength of rotation or shear. The basis of the background visualization is a vector and scalar clustering approach. The local features are defined as the extrema of the respective scalar fields. Applying scalar field topology provides a profound mathematical basis for the feature extraction. All design decisions are guided by the goal of generating a simple to read visualization. To demonstrate the effectiveness of our approach, we show results for three different data sets with different complexity and characteristics.
{"title":"Automatic, tensor-guided illustrative vector field visualization","authors":"Cornelia Auer, Jens Kasten, A. Kratz, E. Zhang, I. Hotz","doi":"10.1109/PacificVis.2013.6596154","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596154","url":null,"abstract":"This paper proposes a vector field visualization, which mimics a sketch-like representation. The visualization combines two major perspectives: Large scale trends based on a strongly simplified field as background visualization and a local visualization highlighting strongly expressed features at their exact position. Each component considers the vector field itself and its spatial derivatives. The derivate is an asymmetric tensor field, which allows the deduction of scalar quantities reflecting distinctive field properties like strength of rotation or shear. The basis of the background visualization is a vector and scalar clustering approach. The local features are defined as the extrema of the respective scalar fields. Applying scalar field topology provides a profound mathematical basis for the feature extraction. All design decisions are guided by the goal of generating a simple to read visualization. To demonstrate the effectiveness of our approach, we show results for three different data sets with different complexity and characteristics.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132291291","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 : 2013-09-12DOI: 10.1109/PACIFICVIS.2013.6596146
Corinna Vehlow, J. Hasenauer, Fabian J Theis, D. Weiskopf
Graphs are used to model relations between sets of objects. Objects are represented by vertices and relations by edges of the graph. Besides vertex-vertex relations, in some application domains also relations between edges exist. Our new visualization approach supports the investigation of both relation types in one diagram. Edge-edge relations are visualized as curves that are directly integrated into the node-link diagram that represents the object-relation structure. In contrast, vertex-vertex relations are illustrated distinguishably from edge-edge relations using straight links as representations. While the shape of links is used to differentiate between the relation types, the weights of the edge-edge relations are mapped to the width and color of the curves. To facilitate an extensive analysis of interrelations, our approach incorporates several interaction techniques that can be used for filtering and highlighting. The usability of our visualization is demonstrated with two case studies in the application domains of bioinformatics and financial services.
{"title":"Visualizing edge-edge relations in graphs","authors":"Corinna Vehlow, J. Hasenauer, Fabian J Theis, D. Weiskopf","doi":"10.1109/PACIFICVIS.2013.6596146","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2013.6596146","url":null,"abstract":"Graphs are used to model relations between sets of objects. Objects are represented by vertices and relations by edges of the graph. Besides vertex-vertex relations, in some application domains also relations between edges exist. Our new visualization approach supports the investigation of both relation types in one diagram. Edge-edge relations are visualized as curves that are directly integrated into the node-link diagram that represents the object-relation structure. In contrast, vertex-vertex relations are illustrated distinguishably from edge-edge relations using straight links as representations. While the shape of links is used to differentiate between the relation types, the weights of the edge-edge relations are mapped to the width and color of the curves. To facilitate an extensive analysis of interrelations, our approach incorporates several interaction techniques that can be used for filtering and highlighting. The usability of our visualization is demonstrated with two case studies in the application domains of bioinformatics and financial services.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130732980","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 : 2013-09-12DOI: 10.1109/PacificVis.2013.6596132
Steven Martin, Han-Wei Shen
Volumetric datasets continue to grow in size, and there is continued demand for interactive analysis on these datasets. Because storage device throughputs are not increasing as quickly, interactive analysis workflows are becoming working set-constrained. In an ideal workflow, the working set complexity of the interactive analysis portion of the workflow should depend primarily on the size of the analysis result being produced, rather than on the size of the data being analyzed. Past works in online analytical processing and visualization have addressed this problem within application-specific contexts, but have not generalized their solutions to a wider variety of visualization applications. We propose a general framework for reducing the working set complexity of the interactive portion of visualization workflows that can be built on top of distribution range queries, as well as a technique within this framework able to support multiple visualization applications. Transformations are applied in the preprocessing phase of the workflow to enable fast, approximate volumetric distribution range queries with low working set complexity. Interactive application algorithms are then adapted to make use of these distribution range queries, enabling efficient interactive workflows on large-scale data. We show that the proposed technique enables these applications to be scaled primarily in terms of the application result dataset size, rather than the input data size, enabling increased interactivity and scalability.
{"title":"Transformations for volumetric range distribution queries","authors":"Steven Martin, Han-Wei Shen","doi":"10.1109/PacificVis.2013.6596132","DOIUrl":"https://doi.org/10.1109/PacificVis.2013.6596132","url":null,"abstract":"Volumetric datasets continue to grow in size, and there is continued demand for interactive analysis on these datasets. Because storage device throughputs are not increasing as quickly, interactive analysis workflows are becoming working set-constrained. In an ideal workflow, the working set complexity of the interactive analysis portion of the workflow should depend primarily on the size of the analysis result being produced, rather than on the size of the data being analyzed. Past works in online analytical processing and visualization have addressed this problem within application-specific contexts, but have not generalized their solutions to a wider variety of visualization applications. We propose a general framework for reducing the working set complexity of the interactive portion of visualization workflows that can be built on top of distribution range queries, as well as a technique within this framework able to support multiple visualization applications. Transformations are applied in the preprocessing phase of the workflow to enable fast, approximate volumetric distribution range queries with low working set complexity. Interactive application algorithms are then adapted to make use of these distribution range queries, enabling efficient interactive workflows on large-scale data. We show that the proposed technique enables these applications to be scaled primarily in terms of the application result dataset size, rather than the input data size, enabling increased interactivity and scalability.","PeriodicalId":179865,"journal":{"name":"2013 IEEE Pacific Visualization Symposium (PacificVis)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121040374","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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