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Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data

机译:表2分析:用于多维数据的常见分析模式的建模与推荐

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Given a table of multi-dimensional data, what analyses would human create to extract information from it? From scientific exploration to business intelligence (BI), this is a key problem to solve towards automation of knowledge discovery and decision making. In this paper, we propose Table2Analysis to learn commonly conducted analysis patterns from large amount of (table, analysis) pairs, and recommend analyses for any given table even not seen before. Multi-dimensional data as input challenges existing model architectures and training techniques to fulfill the task. Based on deep Q-learning with heuristic search, Table2Analysis does table to sequence generation, with each sequence encoding an analysis. Ta-ble2Analysis has 0.78 recall at top-5 and 0.65 recall at top-1 in our evaluation against a large scale spreadsheet corpus on the PivotTable recommendation task.
机译:给定多维数据表,人类的分析会如何从中提取信息? 从科学勘探到商业智能(BI),这是解决知识发现和决策自动化的关键问题。 在本文中,我们提出了从大量(表,分析)对的常见分析模式的情况下,并建议在以前没有看到任何给定表格的分析。 多维数据作为输入挑战现有的模型架构和培训技术来满足任务。 基于Heak Q-Learning与启发式搜索,表2Analysis会对序列生成进行表,每个序列编码分析。 TA-BLE2Analysis在Top-5和0.65次召回的Top-1中的评估中有0.78次召回,在我们对数据透视推荐任务上的大规模电子表格语料库中的评估。

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