Semi-automatic Data Tours to Support Data Exploration and Visualisation Literacy for Novice Analysts

半自动数据之旅支持新手分析师的数据探索和可视化素养

• 批准号: EP/V010662/1
• 负责人: Benjamin Bach
• 金额: $ 33.26万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV010662%2F1
• 关键词: Semi; automatic; Data; Tours; Support

Data analysis is key to understanding timely phenomena from climate change to social media, from diseases to political conflicts, from the human brain to migration. In order to complement statistical analysis and modern machine learning approaches for data analysis, visualisation techniques and interactive interfaces support human-in-the-loop control over these systems as well as human sensemaking in cases where data is uncertain, requires greater overview for the generation of hypotheses, and effective communication to larger audiences. While more and more tools, such as Tableau, Gephi or Microsoft's PowerBI are democratising the use of data visualisation, using data visualisations to their full extend requires training novice analysts in tools, techniques, and interactive exploration, as well as communication and presentation. This project aims to free the analyst from their burden of exploring a data set from the beginning while having to chose among tools, learn their workflows, and create visualisations themselves. Rather, it aims to support novice analysts through a system that automatically displays information about a data set to an analyst while explaining visualisation techniques and findings. In such a "data tour", an analyst starts as a passive reader following a set of visualisations and textual explanations. Respective visualisations will be explained to the analyst. As the analyst becomes familiar with visualisations and their data, they are invited to explore the data by themselves through an interactive interface and communicate the system in which aspects they are most interested in.Creating effective data tours draws inspiration from previous work on using comics for data-driven storytelling (htttp://datacomics.net), visualisation cheatsheets (http://visualizationcheatsheets.github.io) and approaches to data visualisation literacy, data mining for networks, and human-computer interaction. To provide for specific data sets and contact with novice analysts for evaluating our tool, this project involves collaborators in history, archeology, sociology and network science and their complex geo-temporal networks including social networks, archeological trading networks, family networks, and Twitter networks. To create compelling data tours for these data sets we lack significant understanding of - current exploration strategies employed by analysts and their barriers to analysis,- ways of automatically extracting and annotating patterns-of-interest in networks, and- ways of creating meaningful explanatory sequences and high-level structures for data tours.This research involves a coordinated approach of field studies, visualisation and interface design, implementation, and user-centered evaluation. During a brief first phase, we will closely work with experts in Humanities research to create effective visualisations for their networks; in a second phase we mine and present insights from these data sets, and in the last phase, we investigate ways to structure and present findings in data tours. Our research will open new questions in how far storytelling and explaining visualisations can be supported by intelligent agents, i.e., computer programs, that partner with humans and engage in a dialogue. Our research may inspire new forms of intelligent interfaces that foresee an analyst's tasks and understand their specific interest in the data. Researchers in the digital humanities, social sciences, and network analysis will benefit from better support for visualising their geo-temporal networks and semi-automatic ways to analyse and lead to a better understanding of their data and new collaborative research agendas using visual analysis. Our project aims to provide impulses for commercial products and recommendation engines and will provide companies with knowledge and techniques to build customised data tours for their clients.

数据分析是了解从气候变化到社交媒体，从疾病到政治冲突，从人类大脑到移民等及时现象的关键。为了补充数据分析的统计分析和现代机器学习方法，可视化技术和交互式界面支持对这些系统的人在回路控制，以及在数据不确定的情况下的人类感知，需要更全面的概述来生成假设，并与更大的受众进行有效的沟通。虽然越来越多的工具，如Tableau，Gephi或微软的PowerBI正在使数据可视化的使用民主化，但要充分利用数据可视化，需要对新手分析师进行工具，技术和交互式探索以及沟通和演示方面的培训。该项目旨在将分析师从一开始就探索数据集的负担中解放出来，同时不得不选择工具，学习他们的工作流程，并自己创建可视化。相反，它旨在通过一个系统来支持新手分析师，该系统自动向分析师显示有关数据集的信息，同时解释可视化技术和发现。在这样的“数据之旅”中，分析师从一个被动的读者开始，跟随一组可视化和文本解释。将向分析员解释相应的可视化。随着分析师熟悉可视化及其数据，他们被邀请通过交互式界面自行探索数据，并就他们最感兴趣的方面与系统进行交流。创建有效的数据图尔斯之旅从之前的工作中汲取灵感，这些工作包括使用漫画进行数据驱动的故事讲述（http：//www.example.com）、可视化备忘录（http：//www.example.com）以及数据可视化素养、网络数据挖掘和人机交互方法。visualizationcheatsheets.github.io datacomics.net为了提供特定的数据集和接触新手分析师评估我们的工具，这个项目涉及历史，考古学，社会学和网络科学及其复杂的地理时间网络，包括社交网络，考古交易网络，家庭网络和Twitter网络的合作者。为了为这些数据集创建引人注目的数据图尔斯，我们缺乏对以下内容的重要理解：分析师采用的当前探索策略及其分析障碍，自动提取和注释网络中感兴趣模式的方法，以及为数据图尔斯创建有意义的解释序列和高级结构的方法。以用户为中心的评价。在简短的第一阶段，我们将与人文学科研究专家密切合作，为他们的网络创建有效的可视化;在第二阶段，我们从这些数据集中挖掘和呈现见解，在最后一个阶段，我们研究如何在数据图尔斯中构建和呈现结果。我们的研究将开启新的问题，即智能代理可以在多大程度上支持讲故事和解释可视化，计算机程序，与人类合作并参与对话。我们的研究可能会激发新形式的智能界面，预见分析师的任务，并了解他们对数据的具体兴趣。数字人文、社会科学和网络分析领域的研究人员将受益于更好的支持，以可视化他们的地理时空网络和半自动的方式来分析，并使用可视化分析更好地理解他们的数据和新的合作研究议程。我们的项目旨在为商业产品和推荐引擎提供动力，并将为公司提供知识和技术，为客户构建定制的数据图尔斯之旅。

项目成果

NetworkNarratives: Data Tours for Visual Network Exploration and Analysis

• DOI: 10.1145/3544548.3581452
• 发表时间: 2023-03
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Wenchao Li;Sarah Schöttler;James Scott-Brown;Yun Wang;Siming Chen;Huamin Qu;Benjamin Bach

Show Me My Users: A Dashboard Visualizing User Interaction Logs

显示我的用户：可视化用户交互日志的仪表板

• DOI: 10.1109/vis54172.2023.00040
• 发表时间: 2023
• 作者: Wang J

Understanding Barriers to Network Exploration With Visualization: A Report from the Trenches

通过可视化了解网络探索的障碍：来自战壕的报告

• DOI: 10.1109/tvcg.2022.3209487
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: AlKadi M