VLDB 2026 Research / reviewers in the wild / expert
Boxun Zhang
dblp:00/5491
· DBLP profile ↗
5ranked-venue papers
3as first author
2since 2021 · last 2025
0000-0001-6748-4044ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Computer networks · 2 · 2 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 graphics and multimedia
1 paper |
Multimedia systems and quality of experience · 50% Visualization and visual analytics · 50% | |
| Computer networks
1 paper |
Network measurement and analytics · 77% Content delivery and video streaming · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia systems and quality of experience › multimedia streaming
music streaming |
0.2 | 1 | 2013 | Understanding user behavior in Spotify · INFOCOM 2013 |
Visualization and visual analytics
user behavior analysis |
0.2 | 1 | 2013 | Understanding user behavior in Spotify · INFOCOM 2013 |
Network measurement and analytics
workload characterization |
0.2 | 1 | 2013 | Understanding user behavior in Spotify · INFOCOM 2013 |
Content delivery and video streaming › peer-assisted content distribution
peer-assisted streaming |
0.0 | 1 | 2013 | Understanding user behavior in Spotify · INFOCOM 2013 |
Methods — techniques the papers use, named apart from their topics
trace analysis · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Multimodal cross-scale context clusters for classification of mental disorders using functional and structural MRI
Shuqi Yang, Qing Lan, Kuangling Zhang, Guangmin Tang, Jiaqing Miao, Boxun Zhang, Dezhong Yao 0001 |
Neural Networks | 9 |
| 2024 | SCANet: Dual Attention Network for Alzheimer's Disease Diagnosis Based on Gated Residual and Spatial Asymmetry Mechanisms
Donghan Wu, Shuyuan Yang 0008, Zhichang Wang, Shuqi Yang, Boxun Zhang, Jiaqing Miao |
ICANN (8) | 6 |
| 2013 | Understanding user behavior in SpotifyabstractSpotify is a peer-assisted music streaming service that has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, playback arrival patterns, and daily variation of session length. Secondly, we analyze individual user behavior on both multiple and single devices. Our analysis reveals the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. In particular, we conduct the first analysis of the device-switching behavior of a massive user base. Boxun Zhang, Gunnar Kreitz, Marcus Isaksson, Javier Ubillos, Guido Urdaneta, Johan A. Pouwelse, Dick H. J. Epema |
INFOCOM | 1 |
| 2011 | Identifying, analyzing, and modeling flashcrowds in BitTorrentabstractFlashcrowds - sudden surges of user arrivals - do occur in BitTorrent, and they can lead to severe service deprivation. However, very little is known about their occurrence patterns and their characteristics in real-world deployments, and many basic questions about BitTorrent flashcrowds, such as How often do they occur? and How long do they last?, remain unanswered. In this paper, we address these questions by studying three datasets that cover millions of swarms from two of the largest BitTorrent trackers. We first propose a model for BitTorrent flashcrowds and a procedure for identifying, analyzing, and modeling BitTorrent flashcrowds. Then we evaluate quantitatively the impact of flashcrowds on BitTorrent users, and we develop an algorithm that identifies BitTorrent flashcrowds. Finally, we study statistically the properties of BitTorrent flashcrowds identified from our datasets, such as their arrival time, duration, and magnitude, and we investigate the relationship between flashcrowds and swarm growth, and the arrival rate of flashcrowds in BitTorrent trackers. In particular, we find that BitTorrent flashcrowds only occur in very small fractions (0.3-2%) of the swarms but that they can affect over ten million users. Boxun Zhang, Alexandru Iosup, Johan A. Pouwelse, Dick H. J. Epema |
Peer-to-Peer Computing | 1 |
| 2010 | Sampling Bias in BitTorrent Measurements
Boxun Zhang, Alexandru Iosup, Johan A. Pouwelse, Dick H. J. Epema, Henk J. Sips |
Euro-Par (1) | 1 |