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Vis Inf
Vis Inf  2018, Vol. 2 Issue (3): 166-180    DOI: 10.1016/j.visinf.2018.09.003
论文     
人机分析过程导引
Christopher Collinsa, Natalia Andrienkob,i, TobiasSchreckc, JingYangd, Jaegul Chooe, Ulrich Engelkef, Amit Jenag, Tim Dwyerh
aFaculty of Science, University of Ontario Institute of Technology, Ontario, Canada  bFraunhofer Institute IAIS, Sankt-Augustin, Germany  cCGV Institute, Graz University of Technology, Graz, Austria  dDepartment of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA  eDepartment of Computer Science and Engineering, Korea University, Seoul, South Korea  fCSIRO, Hobart, Tasmania, Australia  gIITB-Monash Research Academy, Mumbai, India  hFaculty of Information Technology, Monash University, Melbourne, Australia  iCity, University of London, London, UK
Guidance in the human–machine analytics process
Christopher Collinsa, Natalia Andrienkob,i, TobiasSchreckc, JingYangd, Jaegul Chooe, Ulrich Engelkef, Amit Jenag, Tim Dwyerh
aFaculty of Science, University of Ontario Institute of Technology, Ontario, Canada  bFraunhofer Institute IAIS, Sankt-Augustin, Germany  cCGV Institute, Graz University of Technology, Graz, Austria  dDepartment of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA  eDepartment of Computer Science and Engineering, Korea University, Seoul, South Korea  fCSIRO, Hobart, Tasmania, Australia  gIITB-Monash Research Academy, Mumbai, India  hFaculty of Information Technology, Monash University, Melbourne, Australia  iCity, University of London, London, UK
 全文: PDF 
摘要： 背景：在本文中，我们列出了人机分析的目标、优势和不足之处，提到它不仅可在关键的底层可视化任务中发挥作用,而且能在更复杂的由模型生成的可视化分析任务中发挥作用。人工智能，尤其是在机器学习方面的最新进展，使得人们期盼使用自动技术来实施现由数据分析师采用可视化方法来执行的若干任务。但是，可视化分析仍然非常复杂，包含多个不同的子任务。其中一些任务处于底层，自动化方法可以大显身手（如数据的分类和聚类）；另一些任务则更为抽象，需要用到更多的人类创造力，例如，将从许多不同和异构的数据中获得的思想关联起来，以支持决策。 在本文中，我们将概述导引可能的应用，以及需要为导引提供的输入。我们讨论了实现导引方法的挑战，包括导引系统的输入以及如何为用户提供指导，提出了在分析过程的不同阶段中评估导引质量的潜在方法，并引入导引可能带来的负面效果，作为分析决策的偏差来源。

关键词： 导引;  可视化分析;  模型评估    
Abstract: In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.
Key words: Guidance    Visual analytics    Model evaluation
出版日期: 2018-11-05
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Christopher Collins
Natalia Andrienko
TobiasSchreck
JingYang
Jaegul Choo
Ulrich Engelke
Amit Jena
Tim Dwyer

引用本文:

Christopher Collins, Natalia Andrienko, TobiasSchreck, JingYang, Jaegul Choo, Ulrich Engelke, Amit Jena, Tim Dwyer. Guidance in the human–machine analytics process . Vis Inf, 2018, 2(3): 166-180.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2018.09.003    或    http://www.zjujournals.com/vi/CN/Y2018/V2/I3/166

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