What Users Don't Expect about Exploratory Data Analysis on Approximate Query Processing Systems

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2017-05-14 DOI:10.1145/3077257.3077258

Dominik Moritz, Danyel Fisher

引用次数: 7

Abstract

Pangloss implements "Optimistic Visualization", a method that gives analysts confidence to use approximate results for exploratory data analysis. In this paper, we outline how analysts' experience with an approximate visualization system did not match their intuitions. These observations have implications for the design of future data exploration systems that expose uncertainty. We also describe requirements for approximate query engines to enable the next generation of exploratory visualization systems.

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关于近似查询处理系统的探索性数据分析，用户不期望的是什么

Pangloss实现了“乐观可视化”，这种方法使分析师有信心使用近似结果进行探索性数据分析。在本文中，我们概述了分析师使用近似可视化系统的经验如何与他们的直觉不符。这些观察结果对揭示不确定性的未来数据探索系统的设计具有启示意义。我们还描述了对近似查询引擎的需求，以实现下一代探索性可视化系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)

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