Visualization and data analysis最新文献

{"title":"Morse Decomposition of 3D Piecewise Linear Vector Fields","authors":"Marzieh Berenjkoub, Guoning Chen","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-477","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-477","url":null,"abstract":"Morse decomposition has been shown a reliable way to compute and represent vector field topology. Its computation first converts the original vector field into a directed graph representation, so that flow recurrent dynamics (i.e., Morse sets) can be identified as some strongly connected components of the graph. In this paper, we present a framework that enables the user to efficiently compute Morse decompositions of 3D piecewise linear vector fields defined on regular grids. Specifically, we extend the 2D adaptive edge sampling technique to 3D for the outer approximation computation of the image of any 3D cell for the construction of the directed graph. To achieve finer decomposition, a hierarchical refinement framework is applied to procedurally increase the integration steps and subdivide the underlying grids that contain certain Morse sets. To improve the computational performance, we implement our Morse decomposition framework using CUDA. We have applied our framework to a number of analytic and real-world 3D steady vector fields to demonstrate its utility.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"11 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81085684","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}

{"title":"Star Glyph Insets for Overview Preservation of Multivariate Data","authors":"Dominik Jäckle, J. Fuchs, D. Keim","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-506","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-506","url":null,"abstract":"Exploring vast spatial datasets often requires to drill down in order to inspect details, thus leading to a loss of contextual overview. An additional challenge rises if the visualized data is of multivariate nature, which we encounter in various domains such as healthcare, nutrition, crime reports, or social networks. Existing overview-plus-detail approaches do provide context but only limited support for multivariate data and often suffer from distortion. In this paper, we dynamically integrate star glyphs as insets into the spatial representation of multivariate data thus providing overview while inspecting details. Star glyphs pose an efficient and space saving method to visualize multivariate data, which qualifies them as integrated data representative. Furthermore, we demonstrate the usefulness of our approach in two use cases: The spatial exploration of multivariate crime data collected in San Francisco and the exploration of multivariate whisky data. Introduction Multivariate data accompanies us in our day-to-day life. Prominent examples represent data from healthcare, nutrition, crime reports, or social networks, among others. We typically use spatial representations in order to determine patterns and correlations among dimensions. An example represents the exploration of a huge set of malt whiskies: Each whisky is assigned to the geo-location of its distillery and has several diverse taste categories. The task can be either to seek correlations between particular taste categories and geo-locations, or to find patterns of whiskies for certain taste categories. The latter case can be achieved by applying dimension reduction methods which project the data to a lower dimensional space. When exploring such vast amounts of spatial data, at some point we use zooming and panning interactions to focus on certain regions of interest to obtain a detailed view. However, due to the limited size of the display screen, zooming and panning interactions lead to an inevitable loss of the contextual overview. Overview can be regained by zooming out resulting in a continuous trade-off between overview and detail. Jerding and Stasko argue that the limited size of the display makes it difficult to create efficient global views [25]. Existing Overview-and-Detail and Focus-plus-Context approaches provide comprehensive methods that typically operate in image space. Overview-and-Detail techniques attach a second viewport to the visualization. Although overview is provided, the user is forced to split his attention, which can result in increased cognitive load [19]. In contrast, Focus-plus-Context techniques integrate overview and detail, but use image-based distortion which restricts the interface by means of zooming levels [36]. In this paper, we propose a novel data-driven Off-Screen visualization technique for spatial multivariate data. More specifically, we contribute a dynamic integration of star glyphs as efficient visual insets for the representation of m","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"25 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84604931","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}

{"title":"Interactive Visualization for Interdisciplinary Research","authors":"Naomi Keena, Mohamed Aly Etman, Joshua Draper, P. Pinheiro, A. Dyson","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-491","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-491","url":null,"abstract":"Studies show that many multi-scalar research problems cannot easily be addressed from the confines of individual disciplines for they require the participation of many experts, each viewing the problem from their distinctive disciplinary perspective. The bringing together of disparate experts or fields of expertise is known as interdisciplinary research. The benefit of such an approach is that discourse and collaboration among experts in distinct fields can generate new insights to the research problem at hand. With this approach comes large amounts of multivariate data and understanding the possible relationships between variables and their corresponding relevance to the problem is in itself a challenge. One of the most valuable means through which to comprehend big data and make it more approachable, is through data visualization. This paper presents a trial to encompass an interdisciplinary research centers collaborators, experiments, and results, and represent them simultaneously through the use of a high-resolution visualization. Multiple studies on how best to visualize the multivalent parameters of interdisciplinary work are discussed, highlighting how the use of an interactive data-driven documents (D3) visualization is proving very useful in managing and analyzing the interdisciplinary work of the center in the pursuit of common research goals.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"4 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86412611","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}

{"title":"Enhancing Parallel Coordinates: Statistical Visualizations for Analyzing Soccer Data","authors":"H. Janetzko, M. Stein, D. Sacha, T. Schreck","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-486","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-486","url":null,"abstract":"Visualizing multi-dimensional data in an easy and interpretable way is one of the key features of Parallel Coordinate Plots. However, limitations as overplotting or missing density informations have resulted in many enhancements proposed for Parallel Coordinates. In this paper, we will include density information along each axis for clustered data. The main idea is to visually represent the density distribution of each cluster along the axes. We will show the applicability of our method by analyzing the activity phases of professional soccer players. A final discussion and conclusion will complement this paper.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"6 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79785978","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}

{"title":"Weatherbin: Visually Exploring Similar Days in Air Traffic Weather","authors":"Christopher L Skeels, Kyle I. Murray, K. Kuhn, Akhil Shah","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-485","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-485","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"2 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73171958","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}

{"title":"Multiple Independent Highlighting Techniques","authors":"C. Ware, N. Pioch","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-483","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-483","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"75 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76552725","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}

{"title":"Interactive High-Dimensional Data Analysis Using The \"Three Experts\"","authors":"Georg Albrecht, A. Pang","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-492","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-492","url":null,"abstract":"With the increasing availability of different kinds of data from various domains such as health care, finance, social networks, etc. there is a need to provide analytic tools that are more accessible to lay people. In this paper, we present a software tool which can be used to help make high dimensional data understandable for inexperienced users. To facilitate the understanding of the data, we place special emphasis on how the data is presented, using the ``Three Experts , and on showing personalized information within the data. The Three Experts display shows the results of three different dimension reduction techniques, similar in notion to seeking several expert opinions regarding a particular topic. This will help the user to discern between pertinent structures in the data, and those resulting from the distortion inherent in dimension reduction. The second emphasis is on providing the ability for users to identify, insert, and manipulate points of interest among the sea of data. In addition, the user can observe high dimensional trajectories from one position in the data set to another. This will help convey the changes necessary to displace a point to its desired target state. Observing these changes will enable the user to develop an actionable intuition for the data in question. Though the currently envisioned application for such a system is in health care, such methods could potentially be used in any field where high dimensional data needs to be analyzed.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"493 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77059566","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}

{"title":"Visual Data Mining in Closed Contour Coordinates","authors":"Boris Kovalerchuk, V. Grishin","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-503","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-503","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"143 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78053740","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}

{"title":"Visual Analysis of Transport Similarity in 2D CFD Ensembles","authors":"B. Hollister, A. Pang","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-508","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-508","url":null,"abstract":"Currently, there are no methods of visual analysis for ensemble vector fields (EVF) that provide identification of flow trends and general flow similarity over the extent of transport across ensemble members. Finite-time Variance Analysis (FTVA) provides flow structure information only on particle distributions at the termination of streamline integration. In this paper, we first present a flow structure based on streamline clustering. Second, we discuss a method using streamline clustering to provide information of flow coherence at corresponding spatial regions in the EVF. We consider the regions where bifurcation in flow trends among the EVF members occur. We will also discuss how both methods can be used as a sequential framework for EVF analysis, by using the results of the scalar flow structure to find regions of member flow dissimilarity for further analysis.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"72 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74022810","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}

{"title":"An effective visualization technique for determining co-relations in high-dimensional medieval manuscripts data","authors":"Swati Chandna, D. Tonne, R. Stotzka, H. Busch, Philipp Vanscheidt, Celia Krause","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-488","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-488","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"40 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75106655","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}