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arXiv 1807.06641cs.HC

Beyond Heuristics: Learning Visualization Design

Bahador Saket, Dominik Moritz, Halden Lin, Victor Dibia, Cagatay Demiralp, Jeffrey Heer

英文摘要:

In this paper, we describe a research agenda for deriving design principles directly from data. We argue that it is time to go beyond manually curated and applied visualization design guidelines. We propose learning models of visualization design from data collected using graphical perception studies and build tools powered by the learned models. To achieve this vision, we need to 1) develop scalable methods for collecting training data, 2) collect different forms of training data, 3) advance interpretability of machine learning models, and 4) develop adaptive models that evolve as more data becomes available.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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