FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data
Summary: FusionQuery performs on-demand data fusion using only query-relevant records, avoiding full data-lake scans. It combines line-graph knowledge-graph matching with EM-style veracity/trust estimation and incremental source-trust updates, improving efficiency and effectiveness. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Junhao Zhu (Zhejiang University)
- 2. Yuren Mao (Zhejiang University)
- 3. Lu Chen (Zhejiang University)
- 4. Congcong Ge (Zhejiang University)
- 5. Ziheng Wei (Wuhan University)
- 6. Yunjun Gao (Zhejiang University)
BibTeX Citation
@article{zhu_vldb24,
title = {{FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data}},
author = {Zhu, Junhao and Mao, Yuren and Chen, Lu and Ge, Congcong and Wei, Ziheng and Gao, Yunjun},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {6},
pages = {1337--1349},
doi = {10.14778/3648160.3648174},
url = {https://doi.org/10.14778/3648160.3648174},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,767 | IncreQueryFusion: On-demand Data Fusion Framework in Dynamic Data Lakes | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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