Keynote Talk

Learning in Query Optimization over Knowledge Graphs: From Adaptive Techniques to Neuro-Symbolic Optimizers … And Back?

Query optimization has traditionally relied on the optimize-then-execute paradigm, which, while effective in static settings, faces significant limitations in dynamic and complex environments such as knowledge graphs on the web. In this keynote, I will explore how early database adaptive techniques provided the first steps toward online learning query optimization over knowledge graphs, allowing systems to adjust during execution. I will present results on when adaptivity enhances performance in knowledge graphs and when it falls short.

Following this, I will discuss the rise of neuro-symbolic optimizers—recent innovations that combine machine learning with symbolic processing (e.g., rules, statistics, data summaries, etc.). Therefore, neuro-symbolic optimizers promise to deliver more accurate results than traditional optimizers, especially in the presence of increasingly complex workloads and scenarios. This raises a fundamental question: Can neuro-symbolic optimizers eliminate the need for adaptive techniques, or will systems still require the flexibility to adapt during execution? I will conclude with an exploration of this open question, the challenges of embedding machine learning into query optimization, and the interplay of machine learning and adaptivity, with a special focus on knowledge graphs and their unique requirements.

Bio

Maribel Acosta is the Professor of Data Engineering at the TUM Campus Heilbronn since August 2023. Maribel Acosta studied Computer Science at Universidad Simon Bolivar, Venezuela. From 2012 to 2017, she was a research assistant at the Karlsruhe Institute of Technology (KIT), where she received her doctorate. She then worked as a postdoc and deputy professor at KIT until 2020. Afterward, she was appointed as the professor for Databases and Information Systems at the Ruhr-University Bochum until July 2023. She is actively involved in the scientific communities on Data Management and Artificial Intelligence. Her work has received several “Best Paper Awards” and she serves as chair and reviewer for renowned conferences. Besides research, Maribel Acosta has many years of teaching experience in Databases, Big Data, and Knowledge Graphs and has received two “Best Teaching Awards”. Maribel Acosta investigates techniques for managing knowledge graphs. Her contributions include efficient solutions for querying knowledge graphs while providing high-quality answers.