一对一:信息可视化专题讨论会的可视化

S. Teoh, K. Ma
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引用次数: 3

摘要

我们开发了自己的工具来可视化Infovis专题讨论会的历史。我们称我们的工具为“one - for - all”,因为我们想要生成一张图像来一次有效地回答比赛的所有四个任务(尽管不一定是最佳的)。此外,我们设计的工具是直观的,也就是说,我们希望用户立即理解可视化,即使没有图例或用户指南。这些都是雄心勃勃的目标,但我们认为,一个好的可视化应该满足这些标准。在可视化之前需要进行大量的预处理。首先,我们要合并关键字,例如,我们手动将“hierarchy”和“hierarchies”分组为同一个关键字。接下来,我们找到最重要的关键词,论文数量最多。我们使用这些关键词作为信息可视化的研究领域。我们还注意到,2002年的许多论文没有关键词,所以我们必须根据标题手动插入关键词。我们不允许查阅外部信息,也就是说,我们无法从出版物中找到他们的实际关键字。接下来,我们使用MDS重新排列区域,使交叉引用最多的区域彼此靠近。由于数据非常多,我们根据被引用的次数找到重要的论文/作者,并在我们的可视化中强调它们。在图2的概览可视化中,我们展示了大量的信息,例如(1)重要的关键词及其下的所有论文,(2)相关的研究领域(如;(3)引文,(4)重要作者,(5)重要论文,(6)重要外部论文。一个关键的设计特点是,所有这些非常不同的信息可以清晰地显示在一个显示器上。实现这一点的方法是通过颜色编码,所以如果任何用户对任何特定信息感兴趣,他/她只需要将注意力集中在颜色上。例如,如果你把注意力集中在青色的单词上,你会看到所有的第一作者,而其他的一切都消失了(你自己试试吧!)根据在infois中被引用的次数,重要的论文用更大的圆圈表示。他们的头衔也被贴上了标签。最重要的两篇论文的引用也有突出显示。这样一来,重要的文件就一目了然了。我们可以看到,前两篇论文都是在最早的年份(1995年),而且它们也属于两个非常不同的主题,因为它们只有两篇共同的论文参考它们。该布局由Infovis中研究领域的垂直灰色列组成。含有较多纸张的区域较厚,并以较深的文字标记。我们可以清楚地看到,最重要的领域是“等级制度”,这个领域包含了许多最近的论文。《图形绘制》在2001年有多篇论文。《信息检索》早在1995年就有不少论文,但近年来一直没有论文。与“信息检索”相邻的是“信息分析”,从1996年到1999年有很多论文。这很有趣,因为分析逻辑上紧跟检索,所以它是有意义的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
One-For-All: Visualization of the Information Visualization Symposia
We developed our own tool to visualize the history of the Infovis Symposiums. We call our tool “One-For-All”, because we want to generate one image to at once answer all four tasks of the contest effectively (though not necessarily optimally). Furthermore, we designed the tool to be intuitive, that is, we would like the user to immediately make sense of the visualization, even without a legend or a user guide. These are ambitious goals, but we believe that a good visualization should meet these criteria. Much preprocessing was needed before visualization. First, we have to coalesce the keywords, for example, we manually group “hierarchy” and “hierarchies” as the same keyword. Next, we find the most important keywords, with the most number of papers. We use these keywords as research areas within information visualization. We also notice that many of the 2002 papers do not have keywords, so we have to manually insert keywords according to their titles. We are not allowed to look up external information, that is, we cannot find their actual keywords from the publication. Next, we re-arrange the areas using MDS such that those areas with the most cross-references are placed near to one another. Since there is so much data, we find the important papers/authors, according to how much they are cited, and we emphasize them in our visualization. In the overview visualization of Figure 2, we show a lot of information, for example, (1) important keywords and all the papers under them, (2) related research areas (eg. graph is next to hierarchies), (3) citations, (4) important authors, (5) important papers, and (6) important external papers. A key design feature is that all these very different information can be visualized clearly in one single display. The way this is achieved is through color-coding, so that if any user is interested in any particular information, he/she just has to focus attention on that color. For example, if you focus on the cyan words, you see all the first authors, and everything else fades away (try it out yourself!). The important papers are represented by bigger circles according to how many citations they have within Infovis. Their titles are also labelled. The two most important papers also have their citations highlighted. In this way, the important papers are immediately visible. We can see that the top two papers are both in the earliest year (1995), and they are also in two very different topics, since they only have two common papers refer to them. The layout consists of vertical grey columns of research areas within Infovis. The areas that contain more papers are thicker and are labelled with darker text. We can clearly see that the most important area is “hierarchies” and this area contains many recent papers. “Graph drawing” particularly has many papers in 2001. “Information retrieval” has many papers early in 1995, but no papers in recent years. Adjactent to “Information retrieval” is “Information analysis”, which has many papers from 1996 to 1999. This is interesting because analyisis logically follows retrieval, so it makes sense.
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