Our group won the 2026 CIDR Best Paper Award for our paper, “Survivorship Bias in Industrial Database Workloads.”
As we look at our workload logs now, how can we possibly predict the query the user wants to run, but cannot run?
Marcus et al., “Survivorship Bias in Industrial Database Workloads”
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).
