Dr. Benjamin Hilprecht has been awarded for the prestigious ACM Jim Gray Dissertation Award in 2023

Hilprecht's dissertation, titled “Data-Efficient Learned Database Components,” proposes novel techniques for reducing the high costs of training data collection for learned database components, while still achieving state-of-the-art performance for many database tasks.

He addresses a critical challenge in database design: the need to redesign database components such as query optimizers to meet the increasing variety in workloads, data, and hardware designs. The traditional approach to deriving learned components involves workload-driven learning, which requires the execution of thousands of queries to derive training data. This process is time-consuming and expensive, especially for cloud databases, as it must be repeated for every new database. Hilprecht's dissertation proposes data-efficient learned database components, which reduce or eliminate the high costs of training data collection. The proposed techniques include data-driven learning, zero-shot learning, and transfer learning.

Benjamin Hilprecht completed his Ph.D. (Dr. rer. nat.) at the Data Management Group led by Prof. Dr. Binnig at TU Darmstadt in 2022. Before that, he completed a Master's degree in Mathematics from the Fernuniversität in Hagen and a dual Bachelor's degree in Business Informatics from DHBW Mannheim and SAP SE. After his doctorate, he accepted a position at Everest Systems.

The ACM Special Interest Group on Management of Data(SIGMOD) is concerned with the principles, techniques, and applications of database management systems and data management technology. Our members include software developers, academic and industrial researchers, practitioners, users, and students. SIGMOD sponsors the annual SIGMOD/PODS conference, one of the most important and selective in the field.