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北大可视化暑期学校

北大可视化暑期学校. 分享人： 苏亚博 曾昂. 目录. 1. 暑期学校的日程安排. 2. 课程学习. 3. 相关项目和工具. 暑期学校的日程安排. 重点 面向大数据的高性能可视化与可视分析 时间节点 7月12日-7月13日 可视化研讨会 7月14日-7月17日 可视化课程学习 7月19日 Project Presentation. 每天1个主题，一个主题下有多个topic. 每个小组需要完成一个Project. 课程学习 - Day 1. Xiaoru Yuan - Topics 面向科学计算的大规模数据可视化系统

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北大可视化暑期学校

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  1. 北大可视化暑期学校 分享人： 苏亚博 曾昂

  2. 目录 1 暑期学校的日程安排 2 课程学习 3 相关项目和工具

  3. 暑期学校的日程安排 重点 面向大数据的高性能可视化与可视分析 时间节点 7月12日-7月13日 可视化研讨会 7月14日-7月17日 可视化课程学习 7月19日 Project Presentation 每天1个主题，一个主题下有多个topic 每个小组需要完成一个Project

  4. 课程学习 - Day 1 Xiaoru Yuan - Topics 面向科学计算的大规模数据可视化系统 Content 介绍可视化领域的现状 大规模数据可视化使用的技术、工具： 超级计算机 vs 分布式计算系统（hadoop） Paper： Top 10 challenges in Extreme-Scale Data Visual Analytics

  5. 课程学习 - Day 2 Ye Zhao - 时空数据的可视分析 Topics Text and Document Visual Analytics Big Data and Visual Analytics Text Visualization Word Cloud Based Visualization Time-Varying Visualization Document Visualization Citation Network Visualization Visualizing Streaming Text Biological Visualization Case Study Interactively Discovering Features From Protein Flexibility Matrices Visual Analysis of Tracts of Homozygosity In The Human Genome

  6. 课程学习 - Day 3 Yifan Hu - 大规模图数据的可视化 Topics basic concepts:spring model/MDS, spring-electrical model, Hall's method advanced topic 1: HDE, PivotMDS advanced topic 2: multilevel and fast force approximation advanced topic 3: GMap advanced topic 4: large graph exploration (edge bundling, overlap removal, interaction) advanced topic 5: scalable MDS

  7. 课程学习 - Day 3 Yifan Hu - 大规模图数据的可视化 Paper： 【图绘制算法】 Hu Y. Algorithms for visualizing large networks Harel D, Koren Y. Graph drawing by high-dimensional embedding 【社团聚类算法】 Newman M E J, Girvan M. Finding and evaluating community structure in networks

  8. 课程学习 - Day 4 Claudio Silva - 大规模都市数据的可视分析 Topic Big Data Research at the Center for Urban Science and Progress On April 23, 2012, New York City and New York University announced the inauguration of the Center for Urban Science and Progress (CUSP). CUSP a unique public-private research center that uses New York City as its laboratory and classroom to help cities around the world become more productive, livable, equitable, and resilient. CUSP observes, analyzes, and models cities to optimize outcomes, prototype new solutions, formalize new tools and processes, and develop new expertise/experts. These activities will make CUSP the world’s leading authority in the emerging field of “Urban Informatics.” CUSP is about the intersection of two simple but compelling themes: “cities” and “data”. Research and development at CUSP focuses on the collection, integration, and analysis of data to improve urban systems. In this talk, we will describe CUSP, its research mission, and existing research efforts.

  9. 课程学习 - Day 3 Xiaolong Zhang - Using Geo-visualization Topics 地理信息的可视分析 Books： 【可视分析】 Illuminating the path: The research and development agenda for visual analytics[M]. IEEE Computer Society Press, 2005. Mastering The Information Age-Solving Problems with Visual Analytics[M]. Florian Mansmann, 2010. 【人文】 Motulsky A G. Brave new world?[J]. Science, 1974 Postman N. Amusing ourselves to death: Public discourse in the age of show business[M]. Penguin. com, 2006.

  10. 相关项目和工具 Vast challenge IEEE Vis Tableau

  11. Contents Topic Data Description Our Work

  12. Topics Background 1.All kinds of movies impact of our eyes, it is hard to choose. 2.There are so many information about movies.

  13. Make a Comparison of movies in different years and different types. NO.1 Contrast the movies’ popularity of the same type. Goals NO.2 Contrast different users’ comments on the same movie. NO.3

  14. Contents Topic Data Description Our Work

  15. time Span size dimension snapshots origin Data Description 256M over 60000 movies over 1000000 logs MovieID,Title,Genres,UserID, Tag, Rating, TimeStamp DataTang From 1915 to 2008 Movies.dat Tags.dat Ratings.dat The specific description of data are above.

  16. Data Description Movies.dat Tags.dat Ratings.dat

  17. Contents Topic Data Description Our Work

  18. Our Work Data processing • Clean null film information • Build comprehensive rating information about film • Correlate .dat files to get comments about film Visualization Design 1)Stacked Area Chart view 2)Zoomable Pack Layout view 3) Indented Tree view 4) Word cloud view Achievement • An executable program

  19. Data Processing • Clean null film information(no comment, no rating, •  incomplete film name) • Correlate movies.dat and ratings.dat, build  • comprehensive rating information about film. • Correlate movies.dat and tags.dat to get comments  • about film. • Transform timestamp to comprehensive pattern.

  20. Data Processing Movies, casting time, their genres and ratings. Movies, their genres ,tags and casting time.

  21. Visualization Design • Film information overview

  22. Visualization Design • Top ten information in the specified category

  23. Visualization Design • Film comments in Word Cloud

  24. Visualization Design • Film information in the specified centuries (two different versions)

  25. Thank you

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