Demo Data from VLDB 2026 / reviewers in the wild / expert
Shuaixin Yu
dblp:389/2346
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0008-3728-1172ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 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.
| Computer networks
1 paper |
Content delivery and video streaming · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Content delivery and video streaming
video-on-demand |
0.8 | 1 | 2024 | Magpie: Improving the Efficiency of A/B Tests for Large Scale Video-on-Demand Systems · IMC 2024 |
Methods — techniques the papers use, named apart from their topics
simulation · 1.5a/b testing · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Magpie: Improving the Efficiency of A/B Tests for Large Scale Video-on-Demand SystemsabstractWith the exponential rise in video traffic, researchers and developers require more effective tools to validate the efficacy of designed algorithms for Video-on-Demand (VoD) system. However, traditional experimental platforms face two main challenges: a lack of realistic testing and the need for longer and significant effort. To overcome these limitations, we propose Magpie, an efficient experimental platform tailored for VoD systems. Magpie leverages a realistic operational setting, rapid testing, and high reproducibility to closely simulate online user environments without impacting production systems. Compared to conventional simulations, our evaluation demonstrates that Magpie reduces the disparity with online experiments by 85.6%. Deployed within our company-a leading video content provider in China-Magpie has efficiently validated over tens of algorithms, with 80% demonstrating enhanced performance in subsequent online tests. Hebin Yu, Haiping Wang 0002, Chenfei Tian, Sandesh Dhawaskar Sathyanarayana, Shu Shi, Zhichen Xue, Shuaixin Yu, Yajie Peng, Xiaofei Pang |
IMC | 7 |