VLDB 2026 Research / reviewers in the wild / expert
Qiongwei Ye
dblp:154/3257
· DBLP profile ↗
5ranked-venue papers
0as first author
4since 2021 · last 2024
0000-0002-6948-3894ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 3 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Exploring the impacts of a recommendation system on an e-platform based on consumers' online behavioral data
Qiongwei Ye, Yu Qian 0003, Xinyu Ge |
Inf. Manag. | 3 |
| 2023 | R2V platform's business model reconstruction in the metaverse era: Based on network effects and bundling
Qiongwei Ye |
Inf. Process. Manag. | 2 |
| 2022 | Optimizing Parallel Proof of Vote Consensus Based on Mimic Security in Consortium BlockchainsabstractSome Byzantine fault tolerant (BFT) consensus algorithm in consortium blockchains have lots of message broadcast to encroach bandwidth, which greatly affect the efficiency of consensus execution. Although reducing the number of global consensus nodes can help, it is also at the cost of reduced security. Based on the idea of Mimic Defense, this paper proposes a consensus framework for BFT consensus and takes the Parallel Proof of Vote (PPoV) algorithm as an example to design a randomized node sharding and role partitioning scheme to improve security of the consensus process without affecting efficiency much. In addition, the multimode adjudication group composed of the shard leaders can replace the single leader to complete voting statistics to improve the reliability of consensus results. Through theoretical analysis, it is known that the BFT consensus framework based on mimic security, taking PPoV as an example, has a strong defense effect against eclipse attacks and selfish mining. A simulation experiment is also designed to prove that through efficient iteration and dynamic threshold design, the adjudication group greatly improves the reliability of the results and the robustness of the algorithm. Zhenwei Xiao, Hui Li 0022, Han Wang 0022, Qiongwei Ye, Shusheng Zou, Ping Lu 0008, Qi Lyu |
IEEE Big Data | 5 |
| 2021 | Improving fake news detection with domain-adversarial and graph-attention neural network
Qiongwei Ye, Yu Qian 0003 |
Decis. Support Syst. | 3 |
| 2019 | On detecting business event from the headlines and leads of massive online news articles
Yu Qian 0003, Xiongwen Deng, Qiongwei Ye, Baojun Ma |
Inf. Process. Manag. | 3 |