Building blocks for exploratory data analysis tools

S. Alspaugh, Marti A. Hearst, A. Ganapathi, R. Katz
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引用次数: 7

Abstract

Data exploration is largely manual and labor intensive. Although there are various tools and statistical techniques that can be applied to data sets, there is little help to identify what questions to ask of a data set, let alone what domain knowledge is useful in answering the questions. In this paper, we study user queries against production data sets in Splunk. Specifically, we characterize the interplay between data sets and the operations used to analyze them using latent semantic analysis, and discuss how this characterization serves as a building block for a data analysis recommendation system. This is a work-in-progress paper.
探索性数据分析工具的构建块
数据探索在很大程度上是手工和劳动密集型的。尽管有各种各样的工具和统计技术可以应用于数据集,但对于确定对数据集提出什么问题几乎没有帮助,更不用说在回答这些问题时哪些领域知识是有用的了。在本文中,我们研究了Splunk中针对生产数据集的用户查询。具体来说,我们描述了数据集和使用潜在语义分析来分析它们的操作之间的相互作用,并讨论了这种描述如何作为数据分析推荐系统的构建块。这是一篇正在进行中的论文。
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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