SQL-based data mining algorithms are rarely used in practice today. Most performance experiments have shown that SQL-based approaches are inferior to main-memory algorithms. Nevertheless, database vendors try to integrate analysis functionalities to some extent into their query execution and optimization components in order to narrow the gap between data and processing. Such a database support is particularly important when data mining applications need to analyze very large datasets or when they need access current data, not a possibly outdated copy of it.

We investigate approaches based on SQL for the problem of finding frequent itemsets in a transaction table, including an algorithm that we recently proposed, called Quiver, which employs universal and existential quantifications. This approach employs a table schema for itemsets that is similar to the commonly used vertical layout for transactions: each item of an itemset is stored in a separate row. We argue that expressing the frequent itemset discovery problem using quantifications offers interesting opportunities to process such queries using set containment join or set containment division operators, which are not yet available in commercial database systems. Initial performance experiments reveal that Quiver cannot be processed efficiently by commercial DBMS. However, our experiments with query execution plans that use operators realizing set containment tests suggest that an efficient processing of Quiver is possible.