Guidance in the human-machine analytics process
Guidance in the human-machine analytics process
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DOI：
10.1016/j.visinf.2018.09.003
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发表时间：
2018-09-01
影响因子：
3
通讯作者：
Dwyer, Tim
中科院分区：
文献类型：
--
作者：
Collins, Christopher;Andrienko, Natalia;Dwyer, Tim
在本文中，我们列出了指导的目标和利弊，我们讨论了它不仅在关键的低级可视化任务中，而且在可视化分析的更复杂的模型生成任务中可以发挥的作用。人工智能，特别是机器学习的最新进展，使人们对使用自动技术来执行一些目前由数据分析师使用可视化手动完成的任务的可能性寄予厚望。然而，可视化分析仍然是一项复杂的活动，结合了许多不同的子任务。这些任务中有一些是相对低级的，很明显自动化可以发挥作用，例如，数据的分类和聚类。其他任务则更加抽象，需要大量的人类创造力，例如，将从各种不同和异构的数据工件中收集到的见解联系起来，为决策提供支持。在本文中，我们概述了指导的潜在应用，以及指导的输入。我们讨论了实施指南的挑战，包括对指南系统的输入以及如何为用户提供指导。我们提出了潜在的方法来评估质量的指导在不同阶段的分析过程中，并引入潜在的负面影响的指导作为一个来源的偏见在分析决策。(C)2018浙江大学出版社.由爱思唯尔公司出版
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. (C) 2018 Zhejiang University and Zhejiang University Press. Published by Elsevier B.V.