Demo Data from EDBT 2026 / reviewers in the wild / expert
Yinghao Zou
dblp:295/3379
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Database system architecture and tuning · 33% Indexing and storage engines · 33% Information retrieval · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › similarity search › nearest neighbor search
approximate nearest neighbor search |
0.5 | 1 | 2021 | Milvus: A Purpose-Built Vector Data Management System · SIGMOD Conference 2021 |
Indexing and storage engines › vector index
high-dimensional vector indexing |
0.5 | 1 | 2021 | Milvus: A Purpose-Built Vector Data Management System · SIGMOD Conference 2021 |
Methods — techniques the papers use, named apart from their topics
machine learning feature extraction · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Milvus: A Purpose-Built Vector Data Management SystemabstractRecently, there has been a pressing need to manage high-dimensional vector data in data science and AI applications. This trend is fueled by the proliferation of unstructured data and machine learning (ML), where ML models usually transform unstructured data into feature vectors for data analytics, e.g., product recommendation. Existing systems and algorithms for managing vector data have two limitations: (1) They incur serious performance issue when handling large-scale and dynamic vector data; and (2) They provide limited functionalities that cannot meet the requirements of versatile applications. Jianguo Wang 0001, Xiaomeng Yi, Rentong Guo, Hai Jin 0001, Peng Xu 0003, Shengjun Li, Xiangzhou Guo, Xiaohai Xu, Yuxing Yuan, Yinghao Zou, Jiquan Long, Yudong Cai 0002, Zhenxiang Li, Yihua Mo, Ruiyi Jiang, Charles Xie |
SIGMOD Conference | 13 |