Organizations are collecting increasingly large amounts of data for data driven decision making. These data are often dumped into a centralized repository, e.g., a data lake, consisting of thousands of structured and unstructured datasets. Perversely, such mixture of datasets makes the problem of discovering elements (e.g., tables or documents) that are relevant to a user's query or an analytical task very challenging. Despite the recent efforts in data discovery, the problem remains widely open especially in the two fronts of (1) discovering relationships and relatedness across structured and unstructured datasets where existing techniques suffer from either scalability, being customized for a specific problem type (e.g., entity matching or data integration), or demolishing the structural properties on its way, and (2) developing a holistic system for integrating various similarity measurements and sketches in an effective way to boost the discovery accuracy. In this paper, we propose a new data discovery system, named CMDL, for addressing these two limitations. CMDL supports the data discovery process over both structured and unstructured data while retaining the structural properties of tables.

翻译：各类组织为数据驱动决策正不断收集海量数据。这些数据通常被倾入中央存储库（如数据湖），其中包含数以千计的结构化和非结构化数据集。然而，这种混合数据集使得发现与用户查询或分析任务相关的要素（如表或文档）变得极具挑战性。尽管近期数据发现研究已取得进展，但该问题仍存在两大未解前沿：（1）在跨结构化与非结构化数据集中发现关系与关联性时，现有技术或受限于可扩展性，或仅适用于特定问题类型（如实体匹配或数据集成），又或在处理过程中破坏数据结构特性；（2）缺乏能够有效整合多种相似度测量方法与概要结构以提升发现准确性的全局系统。本文提出名为CMDL的新型数据发现系统以解决上述两个局限。CMDL支持在保留表格结构特性的前提下，对结构化与非结构化数据进行统一数据发现流程。