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Knowledge Generation in Visual Analytics

视觉分析中的知识生成

基本信息

批准号：
350399414
负责人：
Professor Dr. Daniel Keim
金额：
--
依托单位：
Arbeitsgruppe Datenanalyse und Visualisierung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/350399414?language=en
关键词：
Knowledge Generation Visual Analytics

项目摘要

Visual Analytics (VA) combines human and machine capabilities in order to generate knowledge from data. Systems are capable of processing large amounts of data while humans reason about their problems by leveraging their knowledge. Humans participate in the VA process by steering the analysis process through interactions with the system. Most current approaches have two major drawbacks: 1) on the one hand, analysts cannot externalize their expert knowledge during the analysis process; 2) on the other hand, they do not understand the processes happening in the system. The project aims at bringing human and machine closer together in order to enhance VA for a more effective and efficient data analysis. This will be achieved by bridging data and analytic provenance with automated and visualization methods. The two types of provenance information will be used to support and automate human knowledge generation processes adaptively according to the users´ needs in their current analysis stage. A major part of this research proposal is to bridge the gap between human and machine learning (ML) in order to make complex model configuration and interaction more accessible and usable. Further, we aim to mitigate human errors during analysis processes caused by cognitive biases. The machine can be used as an unbiased counter party. The unique feature of this research proposal is a holistic perspective onto the entire knowledge generation process in VA with the goal of enhancing the state of the art by developing methods along the entire VA-pipeline. The investigated methods will be applied to several real world datasets, domains, tasks, and users in the analysis areas of flight trajectories, political debates, and subspace clustering in high-dimensional data. The benefits of this research will be evaluated through user studies illustrating the benefits of the novel methods, which enhance the data analysis process to be more accessible, effective, efficient, transparent, and reliable.

可视化分析(VA)结合了人和机器的能力，以便从数据中生成知识。系统能够处理大量数据，而人类通过利用他们的知识来推理他们的问题。人类通过与系统的交互来引导分析过程，从而参与到分析过程中。目前的大多数方法有两个主要缺陷：1)一方面，分析人员不能在分析过程中外化他们的专业知识；2)另一方面，他们不理解系统中发生的过程。该项目旨在使人和机器更紧密地联系在一起，以增强VA，以便更有效和高效地进行数据分析。这将通过将数据和分析来源与自动化和可视化方法相结合来实现。这两类来源信息将用于根据用户在当前分析阶段的需求自适应地支持和自动化人类知识生成过程。该研究建议的一个主要部分是弥合人类和机器学习(ML)之间的差距，以便使复杂的模型配置和交互更容易获得和使用。此外，我们的目标是减少分析过程中由认知偏差造成的人为错误。这台机器可以用作不偏不倚的对手方。这项研究提案的独特之处在于对退伍军人管理局整个知识生成过程的整体视角，目的是通过沿着整个退伍军人管理局管道开发方法来提高最新水平。所研究的方法将应用于高维数据中飞行轨迹、政治辩论和子空间聚类等分析领域中的几个真实世界数据集、领域、任务和用户。这项研究的好处将通过用户研究来评估，这些研究说明了新方法的好处，这些方法加强了数据分析过程，使其更容易获得、更有效、更高效、更透明和更可靠。