Visualization and data analysis最新文献

{"title":"Closest Point Sparse Octree for Surface Flow Visualization","authors":"Mark Kim, C. Hansen","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-396","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-396","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"14 1","pages":"131-139"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75546371","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":"AssisTag: Seamless Integration of Content-based and Keyword-based Image Exploration for Category Search","authors":"Kazuyo Mizuno, Daisuke Sakamoto, T. Igarashi","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-389","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-389","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"42 1","pages":"58-69"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78181672","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":"Effectiveness of Feature-Driven Storytelling in 3D Time-Varying Data Visualization","authors":"Li Yu, Lane Harrison, Aidong Lu","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-393","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-393","url":null,"abstract":"Storytelling animation has a great potential to be widely adopted by domain scientists for exploring trends in scientific simulations. However, due to the dynamic nature and generation methods of animations, serious concerns have been raised regarding their effectiveness for analytical tasks. This has led to interactive techniques often being favored over animations, as they provide the user with complete control over the visualization. This trend in scientific visualization design has not yet considered newer algorithmic animation generation methods that are driven by the automatic analysis of data features and storytelling techniques. In this work, we performed an experiment which compares feature-driven storytelling animations to common interactive visualization techniques for time-varying scientific simulations. We discuss the design of the experiment, including tasks for storm-surge analysis that are representative of common scientific visualization projects. Our results illustrate the relative advantages of both feature-driven storytelling animations and interactive visualizations, which may provide useful design guidelines for future storytelling and scientific visualization techniques.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"2001 1","pages":"99-109"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88249058","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":"Inferring Partial Orders of Nodes for Hierarchical Network Layout","authors":"Hsiang-Yun Wu, Shigeo Takahashi, H. Miyamura, S. Ohzahata, A. Nakao","doi":"10.2352/ISSN.2470-1173.2017.1.VDA-395","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-395","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"20 1","pages":"118-130"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79088837","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":"The Aleph Data Relation in Structured Data, A Tree within a Tree Visualization","authors":"P. Zellweger","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-499","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-499","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"7 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73079273","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":"Spherical Similarity Explorer for Comparative Case Analysis","authors":"Leishi Zhang, C. Rooney, Lev Nachmanson, W. Wong, B. Kwon, Florian Stoffel, Michael Hund, Nadeem Qazi, Uchit Singh, D. Keim","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-496","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-496","url":null,"abstract":"Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"2 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":"74190989","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-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations","authors":"Lin Shao, D. Sacha, B. Neldner, M. Stein, T. Schreck","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-510","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-510","url":null,"abstract":"Recently, sports analytics has turned into an important research area of visual analytics and may provide interesting findings, such as the best player of the season, for various kinds of sports. Soccer is a very popular and tactical game, which also attracted great attention in the last few years. However, the search for complex game movements is a very crucial and challenging task. We present a system for searching trajectory data in soccer matches by means of an interactive search interface that enables the user to sketch a situation of interest. Furthermore, we apply a domain specific prefiltering process to extract a set of local movement segments, which are similar to a given sketch. Our approach comprises single-trajectory, multi-trajectory, and event-specific search functions based on two different similarity measures. To demonstrate the usefulness of our approach, we define a domain specific task analysis and conduct a case study together with a domain expert from FC Bayern M¨unchen by investigating a real-world soccer match. Finally, we show that multi-trajectory search in combination with event-specific filtering is needed to describe and retrieve complex moves in soccer matches.","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"67 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":"76682328","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":"Visualization Tools for Network Security","authors":"Antoinette E. Attipoe, Jie Yan, Claude Turner, Dwight Richards","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-489","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-489","url":null,"abstract":"","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"12 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":"73643391","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":"End-User Development of Visualizations","authors":"Kostas Pantazos, S. Lauesen","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-482","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-482","url":null,"abstract":"In this article the authors investigated a visualization tool (uVis) for end-user developers, in order to see how end users actually use it. The tool was an early version and the investigation helped the authors to improve it. The investigation showed that users appreciated the simple formula language, the coordinated panels, and the drag-and-drop mechanism. However, the most important thing for them was the immediate response when they changed something, for instance part of a formula. The entire visualization was updated immediately without having to switch from development view to production view. With uVis, developers construct a visualization from simple visual components such as boxes, curvePoints, and textboxes. All component properties such as Top and BackColor can be complex formulas similar to spreadsheet formulas. The operands in the formula can address relational data in a database, other visual objects, and dialog data provided by the user. A special Rows property can bind to a database query and make the component replicate itself for each row in the query. In this way, traditional as well as novel visualizations can be constructed. The most serious usability problems were data binding and not noticing errors (errors were shown in an error list, but not in the formula that had the error). There were many other usability problems. Removing them would speed up learning and make the tool more successful. c © 2016 Society for Imaging Science and","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"106 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":"80756401","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":"FlowVisual: A Visualization App for Teaching and Understanding 3D Flow Field Concepts","authors":"Man Wang, Jun Tao, Jun Ma, Yang Shen, Chaoli Wang","doi":"10.2352/ISSN.2470-1173.2016.1.VDA-476","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-476","url":null,"abstract":"The study of fluid behaviors has been a challenging topic. Flow visualization enables us to visually acquire qualitative and quantitative flow information. There exist various software tools performing different flow visualization tasks. However, we lack tools that help students learn important flow field concepts. In this paper, we present a visualization app, named FlowVisual which runs on iOS devices, to illustrate basic flow field concepts in 3D. In order to meet a comprehensive learning goal for students, we integrate a number of techniques into FlowVisual design, including field-line tracing, field-line comparison, critical point detection and classification, template-based seeding, and surface visualization. We evaluate and demonstrate the effectiveness of FlowVisual by conducting a formal user study including an introduction and training session, an auto-grading test, and a post-questionnaire survey. Introduction Fluid mechanics and computational fluid dynamics (CFD) are among the core courses in many engineering majors such as mechanical engineering, aerospace engineering, biomedical engineering, chemical engineering, and civil engineering. In these courses, it is important for students to acquire the knowledge of fundamental flow field concepts. Many of those concepts are not straightforward to learn. For instance, it is not easy for beginninglevel students to fully understand the differences between various kinds of field-lines and critical points. Commonly, these materials are taught by instructors through explaining concepts and definitions, drawing diagrams and illustrations, and occasionally, playing custom-made animations or video clips. Using intuitive and real flow examples proves to be an excellent way of learning. However, most examples available today are only designed for lecture or demonstration but not for student interaction or selflearning. Developing a pedagogical visualization tool holds the potential to help students better learn these essential flow field concepts through interactive exploration. In this paper, we present FlowVisual, an educational app running on iOS devices, to illustrate basic flow field concepts in 3D. This app is an extension of the desktop version of FlowVisual for 2D flow fields [14]. The desktop version has been used in classroom teaching of CFD course for multiple times and has received positive feedback from students. From our user study, we found that the app helped students with no previous 2D flow field knowledge understand concepts to the similar degree of students who had studied those concepts before. This new mobile FlowVisual is developed to illustrate the concepts in 3D space as cases in 3D are more common yet more challenging to understand in practice. Besides different kinds of field-lines, we also implemented stream surfaces in this app to enrich the perception of the flow field characteristics in a more continuous fashion. Our key deliverable is an app for classroom demonstratio","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"4 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":"74937289","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}