Integrating spreadsheet data via accurate and low-effort extraction
Integrating spreadsheet data via accurate and low-effort extraction
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DOI：
10.1145/2623330.2623617
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发表时间：
2014-08
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影响因子：
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通讯作者：
Zhe Chen;Michael J. Cafarella
中科院分区：
文献类型：
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作者：
Zhe Chen;Michael J. Cafarella
图表包含许多主题的宝贵数据。然而，电子表格很难与其他数据源集成。将电子表格数据转换为关系模型将允许数据分析师使用关系集成工具。我们提出了一个两阶段的半自动系统，提取准确的关系元数据，同时最大限度地减少用户的努力。基于无向图形模型，我们的系统支持下游电子表格集成应用程序。首先，自动提取器使用来自电子表格的图形样式和恢复的元数据的提示来尽可能准确地提取电子表格数据。第二，交互式修复识别分散在大型电子表格语料库中的不同电子表格中的类似区域，允许用户的单个手动修复在许多可能的提取错误上进行摊销。我们的实验表明，一个人可以获得准确的提取，只有31%的手动操作所需的标准分类为基础的技术在两个真实世界的数据集。
Spreadsheets contain valuable data on many topics. However, spreadsheets are difficult to integrate with other data sources. Converting spreadsheet data to the relational model would allow data analysts to use relational integration tools. We propose a two-phase semiautomatic system that extracts accurate relational metadata while minimizing user effort. Based on an undirected graphical model, our system enables downstream spreadsheet integration applications. First, the automatic extractor uses hints from spreadsheets' graphical style and recovered metadata to extract the spreadsheet data as accurately as possible. Second, the interactive repair identifies similar regions in distinct spreadsheets scattered across large spreadsheet corpora, allowing a user's single manual repair to be amortized over many possible extraction errors. Our experiments show that a human can obtain the accurate extraction with just 31% of the manual operations required by a standard classification based technique on two real-world datasets.