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：组织正为数据驱动决策收集日益海量的数据。这些数据常被倾倒入集中式存储库（如数据湖），其中包含数以千计的结构化与非结构化数据集。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元素（如表或文档）变得极为困难。尽管近年来数据发现研究取得进展，但该问题仍面临两大挑战：（1）在跨结构化与非结构化数据集发现关联与相关性方面，现有技术或受限于可扩展性，或仅针对特定问题类型（如实体匹配或数据集成），或在处理过程中破坏数据结构属性；（2）缺乏一个整体性系统来有效整合多种相似性度量与数据摘要，以提升发现准确率。本文提出名为CMDL的新型数据发现系统以解决上述两大局限。CMDL支持对结构化与非结构化数据的发现流程，同时完整保留表格的结构属性。