VLDB 2026 Research / reviewers in the wild / expert
Jianqing Yang
dblp:58/8693
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Information retrieval · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › multimedia analysis and retrieval › music retrieval
music information retrieval |
0.1 | 1 | 2010 | A music search engine for therapeutic gait training · ACM Multimedia 2010 |
Information retrieval › multimedia analysis and retrieval
music retrieval |
0.1 | 1 | 2010 | A music search engine for therapeutic gait training · ACM Multimedia 2010 |
Methods — techniques the papers use, named apart from their topics
user study · 0.2kernel density estimation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | A music search engine for therapeutic gait trainingabstractA music retrieval system is introduced that incorporate tempo, cultural, and beat strength features to help music therapists provide appropriate music for gait training for Parkinson's patients. Unlike current methods available to music therapists (e.g., personal CD/MP3 library search) we propose a domain-specific search engine that utilizes database of music found on YouTube. We independently evaluate the efficacy of our tempo, cultural, and beat strength features on a music database extracted from YouTube. Results from our user study demonstrate the effectiveness and usefulness of our search engine for this application. Qiaoliang Xiang, Jason Hockman, Jianqing Yang, Yu Yi, Ichiro Fujinaga, Ye Wang 0007 |
ACM Multimedia | 4 |