The paper argues that workload traces observed in industrial settings represent a negotiation between the data platform and the platform’s users. Users mold their queries to run well on the platform, and engineers tune the platform to better meet user demands. This cycle, while great for both users and data platforms, creates a survivorship bias in the observed workloads: the most frequently processed queries are precisely the queries that are already working well.

The paper’s authors include Ryan Marcus (assistant professor), Jeffrey Tao (PhD student), Peizhi Wu (PhD student alumnus), and Zijie Zhao (PhD student).

Congratulations to Professor Ryan Marcus, for receiving a 2025 Google ML and Systems Junior Faculty Award. This is a recognition of both the promise and impact of Ryan’s work on using machine learning techniques to accelerate tasks in data management!

Ph.D. student Soonbo Han (advisor: Zachary Ives) presented his work titled “Implementation Strategies for Views over Property Graphs,” which won the best paper award! Soonbo’s work shows query rewriting techniques can take advantage of semantic views over graph data, including how to index and maintain such views dynamically.

Ph.D. student Jiaming Liang (advisor: Zachary Ives) presented his work titled “RITA: Group Attention is All You Need for Timeseries Analytics.” Jiaming’s work shows how careful grouping and caching of semantically-similar inputs can accelerate neural attention mechanisms, allowing attention networks to scale up to previously-impossible tasks.

Due to visa issues, Zack presented a paper from Ph.D. student Peizhi Wu, titled “Modeling Shifting Workloads for Learned Database Systems.” Peizhi’s work shows how to keep learned database components up to date with data drift using a carefully-tuned replay buffer.

Visiting Ph.D. student Yao Tian (advisor: Xiaofang Zhou, Penn supervisors: Zachary Ives and Ryan Marcus) presented her work titled “A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams.” Yao’s work combines traditional Cuckoo filters with deep learning models to enable approximate membership queries over dynamically-sized windows, achieving significantly higher accuracy than previous results.

At the aiDM workshop, Ph.D. student Zixuan Yi (advisor: Ryan Marcus and Zachary Ives) presented her work titled “Low Rank Approximation for Learned Query Optimization.” Zixuan’s work shows how linear methods for approximating low rank matrices can be used to learn to steer an entire query workload at once.

Later this summer, Penn will present several papers at VLDB in Guangzhou, China!