Systemic view of human-data interaction: analyzing a COVID-19 data visualization platform
Systemic view of human-data interaction: analyzing a COVID-19 data visualization platform
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人与数据交互的系统视图：分析 COVID-19 数据可视化平台
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
R. Pereira
中科院分区：
文献类型：
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作者：
Bernardo Ferrari;D. P. Silva Junior;R. Pereira
人机交互（HDI）是一个不断发展的领域，它将人置于数据流的中心，并为人们与系统和数据进行明确的交互提供机制。从社会技术角度理解 HDI，我们认为必须在整个数据生命周期中以相互关联的方式解决技术和人类问题。在本文中，我们将讨论建立在名为“扩展符号学框架”的概念工件上，讨论不同的利益相关方、不同级别的符号以及数据生命周期的不同阶段如何影响 HDI。我们将此工件应用到当地的 COVID-19 信息网站中，并使用其结果为网站重新设计提供信息。我们的讨论和结果表明，扩展符号学框架能够促进考虑人类和技术问题的系统观点，以及识别不同数据阶段的问题和挑战。
Human-Data Interaction (HDI) is a growing field concerned by placing humans at the center of data flows and providing mechanisms for people to interact explicitly with systems and data. Understanding HDI from a sociotechnical perspective, we argue that technical and human issues must be approached in an interconnected way throughout a data lifecycle. In this paper, grounding our discussions in a conceptual artifact named Extended Semiotic Framework, we discuss how different interested parties, different levels of signs, and different stages in data lifecycle can affect HDI. We apply this artifact into a local COVID-19 information website and use its results to inform the website redesign. Our discussion and results show the Extended Semiotic Framework as capable to promote a systemic view considering both human and technical issues, as well as to identify problems and challenges at different data stages.