III: Small: Extracting Data and Structure from Charts and Graphs for Analysis, Reuse and Indexing

III：小：从图表和图形中提取数据和结构以进行分析、重用和索引

基本信息

批准号：
1714647
负责人：
Maneesh Agrawala
金额：
$ 49.94万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714647&HistoricalAwards=false
关键词：
III Small Extracting Data Structure

项目摘要

Charts and graphs are ubiquitous forms of data representations, appearing in scientific papers, textbooks, reports, news articles and webpages. These visualizations leverage human visual processing to efficiently convey large amounts of quantitative information, and to illustrate trends and differences in the data. But, while people can easily interpret data from charts and graphs, machines cannot directly access this data. Today, a vast trove of information is locked inside data visualizations. This proposal will develop techniques for extracting data and structure from such visualizations and thereby enable data further analysis, reuse and new forms of indexing across the collection of existing charts and graphs. Some of the applications will be specifically designed to improve the accessibility of visualizations for visual impaired users. The tools will provide a novel computational infrastructure for knowledge integration and sharing and impact a broad range of users including scientists, journalists, economists, social scientists, and educators. Specifically this proposal addresses three main goals. First, it develops computational models for interpreting visualizations to extract the underlying data, graphical marks, and mappings that relate the data to mark attributes. The approach will be informed by recent work on human perception and cognition of visualizations. The aim is to build generalized computational models that can accurately extract data from visualizations and also mimic the way people decode information from visualizations. Second, it supports development of a suite of applications that enable analysis and repurposing of visualizations and data. Third, it applies automated visualization interpretation techniques at Internet scale and develops a search engine that indexes visualizations based on their underlying data and graphical structure. The search engine will accelerate data-driven analysis and discovery by facilitating browsing and retrieval of data that is currently locked in computationally inaccessible visualizations. The project website will include information on the project and provide access to resulting publications, software and datasets.

图表和图形是数据表示的无处不在形式，出现在科学论文，教科书，报告，新闻文章和网页中。这些可视化利用人类的视觉处理有效地传达了大量的定量信息，并说明了数据的趋势和差异。但是，尽管人们可以轻松地从图表和图表中解释数据，但计算机无法直接访问此数据。如今，大量信息已锁定在数据可视化中。该建议将开发从此类可视化中提取数据和结构的技术，从而促进数据在现有图表和图表的集合中进一步分析，再利用和新形式的索引形式。某些应用程序将专门设计，以提高视觉受损用户可视化的可访问性。这些工具将为知识整合和共享并影响包括科学家，记者，经济学家，社会科学家和教育工作者在内的广泛用户提供新颖的计算基础架构。具体而言，该提案解决了三个主要目标。首先，它开发了用于解释可视化的计算模型，以提取将数据与标记属性相关联的基本数据，图形标记和映射。该方法将通过最近关于人类感知和可视化认知的工作来告知这种方法。目的是建立通用的计算模型，该模型可以准确地从可视化中提取数据，并模仿人们从可视化中解码信息的方式。其次，它支持开发一套应用程序，以实现可视化和数据的分析和重新利用。第三，它在Internet规模上采用自动可视化解释技术，并开发了搜索引擎，该搜索引擎基于其基础数据和图形结构来索引可视化。搜索引擎将通过促进浏览和检索目前锁定在计算上无法访问的可视化中来加速数据驱动的分析和发现。项目网站将包括有关项目的信息，并提供对所得出版物，软件和数据集的访问。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Answering Questions about Charts and Generating Visual Explanations

DOI：
10.1145/3313831.3376467
发表时间：
2020-04
期刊：
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
影响因子：
0
作者：
Dae Hyun Kim;Enamul Hoque;Maneesh Agrawala
通讯作者：
Dae Hyun Kim;Enamul Hoque;Maneesh Agrawala

Searching the Visual Style and Structure of D3 Visualizations

DOI：
10.1109/tvcg.2019.2934431
发表时间：
2019-07
期刊：
IEEE Transactions on Visualization and Computer Graphics
影响因子：
5.2
作者：
Enamul Hoque;Maneesh Agrawala
通讯作者：
Enamul Hoque;Maneesh Agrawala

Converting Basic D3 Charts into Reusable Style Templates

DOI：
10.1109/tvcg.2017.2659744
发表时间：
2016-09
期刊：
IEEE Transactions on Visualization and Computer Graphics
影响因子：
5.2
作者：
Jonathan Harper;Maneesh Agrawala
通讯作者：
Jonathan Harper;Maneesh Agrawala

Facilitating Document Reading by Linking Text and Tables

DOI：
10.1145/3242587.3242617
发表时间：
2018-10
期刊：
Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology
影响因子：
0
作者：
Dae Hyun Kim;Enamul Hoque;Juho Kim;Maneesh Agrawala
通讯作者：
Dae Hyun Kim;Enamul Hoque;Juho Kim;Maneesh Agrawala

Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts

了解读者如何整合图表和标题：折线图案例研究

DOI：
10.1145/3411764.3445443
发表时间：
2021
期刊：
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
影响因子：
0
作者：
Kim, Dae Hyun;Setlur, Vidya;Agrawala, Maneesh
通讯作者：
Agrawala, Maneesh