7 citations · 8 across the 5 of their papers we have counts for
5 papers
KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang +16
Discovering insights from a real-world data lake potentially containing unclean, semi-structured, and unstructured data requires a variety of data processing tasks, ranging from ex…
Improving DBMS Scheduling Decisions with Fine-grained Performance Prediction on Concurrent Queries -- Extended
Ziniu Wu, Markos Markakis, Chunwei Liu +4
Query scheduling is a critical task that directly impacts query performance in database management systems (DBMS). Deeply integrated schedulers, which require changes to DBMS inter…
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD -- Extended Version
Geoffrey X. Yu, Ziniu Wu, Ferdi Kossmann +5
Modern organizations manage their data with a wide variety of specialized cloud database engines (e.g., Aurora, BigQuery, etc.). However, designing and managing such infrastructure…
CascadeServe: Unlocking Model Cascades for Inference Serving
Ferdi Kossmann, Ziniu Wu, Alex Turk +3
Machine learning (ML) models are increasingly deployed to production, calling for efficient inference serving systems. Efficient inference serving is complicated by two challenges:…
Stage: Query Execution Time Prediction in Amazon Redshift
Ziniu Wu, Ryan Marcus, Zhengchun Liu +7
Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution…