7 citations · 11 across the 7 of their papers we have counts for
8 papers
Quantum Work Statistics from Entanglement-Assisted Invariance
Akira Sone, Akram Touil, Emery Doucet +1
We demonstrate that the one-time measurement scheme is more consistent than the standard two-time measurement scheme with respect to the fundamental symmetry of entanglement, namel…
EXPLAIN Yourself! Finding Query Planner Stalls Across DBMSes
Geoffrey X. Yu, Ryan Marcus, Tim Kraska
Query planners are typically expected to produce optimized plans quickly, leading many researchers (including the authors of this paper) and practitioners to design systems that as…
JetStream: Generating Query Accelerators for Existing Database Systems
Akhilesh Balasingam, Amadou Ngom, Geoffrey X. Yu +1
Recent work has shown that LLMs can synthesize highly specialized database systems for fixed workloads, but existing approaches typically assume static data and replace the databas…
Tailwind: A Practical Framework for Query Accelerators
Geoffrey X. Yu, Ryan Marcus, Tim Kraska
Relational database management systems (RDBMSes) can process general-purpose queries, but often have lower performance compared to custom-built solutions for specific queries. For…
PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking
Yan Zhou, Chunwei Liu, Bhuvan Urgaonkar +11
Cloud service providers commonly use standard benchmarks like TPC-H and TPC-DS to evaluate and optimize cloud data analytics systems. However, these benchmarks rely on fixed query…
The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…
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…
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Wenqi Jiang, Shuai Zhang, Boran Han +3
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large da…