Visualization and data analysis最新文献

{"title":"Exploiting Regions of Influence to Visualize Class Boundaries","authors":"Pallav Tinna, K. Karlapalem","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-500","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-500","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"1 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":"78724472","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":"TennisMatchViz: A Tennis Match Visualization System","authors":"Xi He, Ying Zhu","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-504","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-504","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"63 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":"89855654","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":"Supporting hypotheses management during asynchronous collaboration for visual analytics for text","authors":"Ankit Gupta, Chris D. Shaw","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-502","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-502","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"2 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":"87123979","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":"JASPER: Just A new Space-filling and Pixel-oriented layout for large graph ovERview","authors":"J. Vallet, G. Melançon, Bruno Pinaud","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-484","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-484","url":null,"abstract":"When analysing data and handling a visualisation, users mainly spend their cognitive resources making sense of the graph-ical representation and mapping it back to the data and domain. This task becomes even more critical when dealing with larger data sets. Therefore, a valuable visualisation design strategy is to couple graphical representations and user tasks to better support the sense making process. This paper focuses on a particular task where users must make sense of state changes occurring on nodes of a graph. To this end, we propose JASPER, a new layout algorithm focusing on the visualisation of nodes inspired from pixel-oriented layouts, relying on node clustering to identify and represent existing connections through spatial adjacency. JASPER can layout moderate size graphs in real-time and is able to tackle large graphs with up to 2 million nodes and 5 million edges in reasonable time (about half a minute). Furthermore, although JASPER has been designed around a specific application , the underlying methodology can be employed to draw quick overviews of any type of graphs. The paper lays down the underlying principles of JASPER, and reports it performances (execution times) on increasingly large graphs. JASPER is then used and showcased to visualise network propagation phenomenon in large graphs.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"47 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":"87054828","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 Visual Analytics in Support of Image-Encoded LiDAR Analysis","authors":"Todd Eaglin, Xiaoyu Wang, W. Ribarsky","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-495","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-495","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"5 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":"88428048","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":"TRI-Direct: Interactive Visual Analysis of TRI Data","authors":"David Burlinson, K. Subramanian, Aidong Lu","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-494","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-494","url":null,"abstract":"In this work, we present TRI-DIRECT, an interactive visual analytic system with capabilities to analyze spatio-temporal data for both professional and novice users. The system is motivated by the Toxic Release Inventory (TRI) program of US Environmental Protection Agency (EPA) and its associated datasets; the TRI program was created in 1986 to track toxic chemical usage, which includes release, recycling, treatment and recovery, and its impact on the environment. The design of the system is motivated for ease of use and its future transition to mobile platforms, so as to have the widest possible impact across users possessing a range of skills/interests. We describe TRI-DIRECT’s design, implementation and capabilities, and present two detailed use cases with the system, (1) Texas vs. Louisiana’s usage and processing of toxic chemicals, and, (2) comparing an urban region, Raleigh, NC, vs. the state of North Carolina.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"59 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":"84281109","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":"Paper: Togpu: Automatic Source Transformation from C++ to CUDA using Clang/LLVM","authors":"Matthew Marangoni, T. Wischgoll","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-487","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-487","url":null,"abstract":"Parallel processing using GPUs provides substantial increases in algorithm performance across many disciplines including image processing. Serial algorithms are commonly translated to parallel CUDA or OpenCL algorithms. To perform this translation a user must first overcome various GPU development entry barriers. These obstacles change depending on the user but in general may include learning to program using the chosen API, understanding the intricacies of parallel processing and optimization, and other issues such as the upkeep of two sets of code. Such barriers are experienced by experts and novices alike. Leveraging the unique source to source transformation tools provided by Clang/LLVM we have created a tool to generate CUDA from C++. Such transformations reduce obstacles experienced in developing GPU software and can increase efficiency and revision speed regardless of experience. Image processing algorithms specifically benefit greatly from a quick revision cycle which our tool facilitates. This manuscript details togpu, an open source, cross platform tool which performs C++ to CUDA source to source transformations. We present experimentation results using common image processing algorithms. The tool lowers entrance barriers while preserving a singular code base and readability. Providing core tools enhancing GPU developer facilitates further developments to improve high performance, parallel computing.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"48 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":"76370857","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":"MaVis: Machine Learning Aided Multi-Model Framework for Time Series Visual Analytics","authors":"Kaiyu Zhao, M. Ward, Elke A. Rundensteiner, H. N. Higgins","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-493","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-493","url":null,"abstract":"The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the continuously growing scale of the data nowadays challenges the traditional data analytics in the ”big-data” era. Particularly, the human cognitive capabilities are constant whereas the data scale is not. Furthermore, most existing work focus on how to extract interesting information and present that to the user while not emphasizing on how to provide options to the analysts if the extracted information is not interesting. In this paper, we propose a visual analytic tool called MaVis that integrates multiple machine learning models with a plug-andplay style to describe the input data. It allows the analysts to choose the way they prefer to summarize the data. The MaVis framework provides multiple linked analytic spaces for interpretation at different levels. The low level data space handles data binning strategy while the high level model space handles model summarizations (i.e. clusters or trends). MaVis also supports model analytics that visualize the summarized patterns and compare and contrast them. This framework is shown to provide several novel methods of investigating co-movement patterns of timeseries dataset which is a common interest of medical sciences, finance, business and engineering alike. Lastly we demonstrate the usefulness of our framework via case study and user study using a stock price dataset.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"13 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":"75605328","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 Descriptors for Dense Tensor Fields in Computational Turbulent Combustion: A Case Study","authors":"G. Marai, T. Luciani, A. Maries, S. L. Yilmaz, M. Nik","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-507","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-507","url":null,"abstract":"Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involve solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this article, the authors analyze the challenges in dense symmetric-tensor visualization as applied to turbulent combustion calculation; most notable among these challenges are the dataset size and density. They analyze, together with domain experts, the feasibility of using several established tensor visualization techniques in this application domain. They further examine and propose visual descriptors for volume rendering of the data. Of these novel descriptors, one is a density-gradient descriptor which results in Schlieren-style images, and another one is a classification descriptor inspired by machine-learning techniques. The result is a hybrid visual analysis tool to be utilized in the debugging, benchmarking and verification of models and solutions in turbulent combustion. The authors demonstrate this analysis tool on two example configurations, report feedback from combustion researchers, and summarize the design lessons learned. c © 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010404] INTRODUCTION Computational simulation of turbulent combustion for gas turbine design has become increasingly important in the last two decades, due in part to environmental concerns and regulations on toxic emissions. Such modern gas turbine designs feature a variety of mixing fuel compositions and possible flow configurations,1,2 which make non-computational simulations difficult. The focus of the computational research effort in this direction is on the development of computational tools for the modeling and prediction of turbulent combustion flows. Received June 30, 2015; accepted for publication Nov. 4, 2015; published online Dec. 10, 2015. Associate Editor: Song Zhang. 1062-3701/2016/60(1)/010404/11/$25.00 Tensor quantities are common features in these turbulent combustion models. In particular, stress and strain tensors are often correlated to turbulent quantities—which appear unclosed in the mathematical formulation and thus need to be modeled as part of the computational simulation. Visual identification of the characteristics of such tensor quantities can bring significant insights into the computational modeling process. However, these computational tensor fields are very large and spatially dense—a good example of the Big Data revolution across sciences and engineering. Figure 1 shows an example turbulent combustion configuration, featuring a grid size of 106 and 6× 106 particles (shown as spheres); this dataset should be considered in contrast to traditional tensor datasets, which feature grid sizes in the 102 range. At such large scales, typical glyph en","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"28 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":"88601030","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":"Ensemble Traces: Interactive Visualization of Ensemble Multivariate Time Series Data","authors":"Swastik Singh, Song Zhang, W. Pruett, R. Hester","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-505","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-505","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"6 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":"73723162","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}