Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph

Understand

Business Intelligence (BI) is crucial in modern enterprises and billion-dollar business.

• Traditionally, technical experts like database administrators would manually prepare BI-models (e.g., in star or snowflake schemas) that join tables in data warehouses, before less-technical business users can run analytics using end-user dashboarding tools.
• However, the popularity of self-service BI (e.g., Tableau and Power-BI) in recent years creates a strong demand for less technical end-users to build BI-models themselves.
• We develop an Auto-BI system that can accurately predict BI models given a set of input tables, using a principled graph-based optimization problem we propose called \textit{k-Min-Cost-Arborescence} (k-MCA), which holistically considers both local join prediction and global schema-graph structures, leveraging a graph-theoretical structure called \textit{arborescence}.