一种轻量级的数据表深层关系挖掘算法
A Lightweight Algorithm to Uncover Deep Relationships in Data Tables
摘要：
我们今天收集的许多数据都是表格形式的，行是记录，列是与每个记录相关联的属性。理解表格数据中的结构关系可以极大地促进数据科学过程。传统上，这些关系信息大部分存储在表模式中，并由其创建者（通常是领域专家）维护。在本文中，我们开发了自动化的方法来发现在一个单一的数据表，没有专家或领域知识的深层关系。我们的方法可以将数据表分解成更小的表层，揭示其深层结构。我们的方法的关键是一个计算轻量级的前向加法算法，我们开发递归提取表列之间的函数依赖关系，可扩展到表与许多列。通过我们的解决方案，数据科学家在探索新数据集时将获得自动生成的数据驱动见解。
英文摘要：
Many data we collect today are in tabular form, with rows as records and columns as attributes associated with each record. Understanding the structural relationship in tabular data can greatly facilitate the data science process. Traditionally, much of this relational information is stored in table schema and maintained by its creators, usually domain experts. In this paper, we develop automated methods to uncover deep relationships in a single data table without expert or domain knowledge. Our method can decompose a data table into layers of smaller tables, revealing its deep structure. The key to our approach is a computationally lightweight forward addition algorithm that we developed to recursively extract the functional dependencies between table columns that are scalable to tables with many columns. With our solution, data scientists will be provided with automatically generated, data-driven insights when exploring new data sets.
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。
