Query-driven explainable join discovery of analytic cubes based on profiling

The rapid growth of data repositories limits user's awareness of their content and the understanding of interrelationships between diverse datasets, increasing the demand for effective data discovery tools that help analysts locate relevant data. Join discovery, the process of finding tables that can be joined based on common attributes, is one possible strategy. This paper introduces a novel join discovery approach tailored to data structures commonly used in analytics: multidimensional or analytic cubes. By leveraging the unique structure of analytic cubes, the approach utilizes the concept of profile, which refers to metadata providing information about data distribution. Specifically, we introduce a profile-based, query-driven join discovery model that allows users to identify alternative joinable cubes starting from a declarative query, as well as to rank and qualitatively assess alternatives. We validate the efficiency and effectiveness of our approach through extensive experimentation. Our dual approach, which combines schema-level and content-level information, enhances both the expressivity of join discovery and the clarity of join discovery results, making the analysis process more intuitive and interpretable.