Towards Efficient Semantic Query Processing Engines

Abstract: Large language models (LLMs) have expanded the scope of databases from purely relational to semantic query processing engines. These engines support SQL queries with AI operators on multimodal data, configured via natural language instructions, that process functions requiring deep semantic understanding of data.  Semantic query processing has been adopted quickly in industry. However, the high overheads of LLM invocations limit its scalability.

In my talk, I will focus on methods that make semantic query processing more efficient. First, I will present SemBench, the "TPC-H of semantic query processing", enabling us to benchmark cost-quality tradeoffs realized by semantic query processing engines. Second, I will discuss our work on ThalamusDB, leveraging ideas from approximate query processing and specialized semantic operator implementations to speed up semantic queries. I will conclude by describing ongoing projects and avenues for future research.

Bio: Immanuel Trummer is an associate professor of computer science at Cornell University. His research focuses on making data analysis more efficient and more user-friendly, often leveraging techniques from the area of machine learning, in particular, large language models. His papers were selected for "Best of VLDB", "Best of SIGMOD", and the CACM Research Highlight Award. He received an NSF CAREER grant for his work on database tuning via LLMs and multiple Google Faculty Research Awards. He is also the author of the book "Data Analysis with LLMs", now available in five languages.