Demo Data from EDBT 2026 / reviewers in the wild / expert
Victor Fu
dblp:328/6205
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Query processing and optimization · 50% Database system architecture and tuning · 50% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing
GPU query processing |
0.2 | 1 | 2022 | Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem · Proc. VLDB Endow. 2022 |
Methods — techniques the papers use, named apart from their topics
tensor compilation · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | The Tensor Data Platform: Towards an AI-centric Database System
Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi |
CIDR | 3 |
| 2022 | Share the Tensor Tea: How Databases can Leverage the Machine Learning EcosystemabstractWe demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to supports the TPC-H benchmark, and it provides performance that are comparable to, and often better than, that of specialized CPU and GPU query processors. Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi |
Proc. VLDB Endow. | 2 |