Please note: This master’s thesis presentation will take place in DC 2314.
Boyi Li, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Xiao Hu
Antijoin, given its great expressive power, sees many applications in relational data analytics. Notwithstanding its importance, there remain great research potentials in antijoin processing. In practical database systems, existing techniques to process antijoins are still considered rudimentary, building upon heuristics and cost-based optimization strategies that offer no theoretical guarantees. Meanwhile, the database theory community has proposed algorithms for antijoins with strong theoretical guarantees, yet these algorithms build upon specialized, complicated data structures and have not made their way to practice.
In light of such gap between theory and practice, we propose new algorithms for antijoin processing in this thesis. Not only do our new algorithms provide the same theoretical guarantees as the state-of-the-art algorithm, but they also use only basic relational operations. The latter property enables our new algorithms to be rewritten in basic SQL statements, allowing an easy, system-agnostic integration into any SQL-based database system. We then empirically evaluate one of our new algorithms, rewritten in SQL, over real-life graph datasets with a variety of SQL database systems. Experimental results show order-of-magnitude improvements of our new algorithm over vanilla SQL queries.