Recursive SQL for Data Mining

To implement algorithms within database systems beyond the design of SQL as a data query language, library functions or external tools were used that require the extraction of data first. To eliminate the need of data extraction out of database systems, we argue that SQL-92 plus recursive tables is capable of expressing user-defined algorithms. To underline this claim, we transform selected algorithms out of graph mining, clustering and association rule analysis into recursive common table expressions (CTEs). We compare their performance to the one of user-defined functions and external tools. Our evaluation shows a competitive performance when using recursive CTEs to library functions either when using a disk-based database systems or a modern in-memory engine.