IncreQueryFusion: On-demand Data Fusion Framework in Dynamic Data Lakes

Summary: IncreQueryFusion enables incremental, on-demand fusion in dynamic data lakes via temporal-index evidence retrieval, hierarchical truth inference, and dynamic truth maintenance. It improves accuracy 5–30% and delivers >10× speedups over on-demand and >100× over batch methods. (summarized by gpt-5.6-luna on Aug 17 2026)

Paper ID: h682d6bf747a7105b
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,767 | 27.61%
DOI: 10.14778/3819518.3819532

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Wenhao Liu (Zhejiang University)
2. Sai Wu (Zhejiang University)
3. Xiu Tang (Zhejiang University)
4. Yitong Zhang (Zhejiang University)
5. Dong Peng (Ant Financial)
6. Guolong Huang (Ant Financial)
7. Gang Chen (Zhejiang University)

BibTeX Citation

@article{liu_vldb26,
        title = {{IncreQueryFusion: On-demand Data Fusion Framework in Dynamic Data Lakes}},
        author = {Liu, Wenhao and Wu, Sai and Tang, Xiu and Zhang, Yitong and Peng, Dong and Huang, Guolong and Chen, Gang},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {9},
        pages = {2032--2044},
        doi = {10.14778/3819518.3819532},
        url = {https://doi.org/10.14778/3819518.3819532},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
20	Similarity Search in High Dimensions via Hashing	1999	VLDB	0.00057568153
325	The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing	2015	VLDB	0.00020964941
504	A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration	2012	VLDB	0.00017164466
624	Data Lake Management: Challenges and Opportunities	2019	VLDB	0.00015479826
877	Integrating Conflicting Data: The Role of Source Dependence	2009	VLDB	0.00013306717
938	Truth Finding on the Deep Web: Is the Problem Solved?	2013	VLDB	0.00012973266
1,123	Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark	2018	SIGMOD	0.0001193233
1,385	Truth Discovery and Copying Detection in a Dynamic World	2009	VLDB	0.00010843714
1,754	Data Fusion – Resolving Data Conflicts for Integration	2009	VLDB	9.7241105e-05
3,323	A Confidence-Aware Approach for Truth Discovery on Long-Tail Data	2015	VLDB	7.4291935e-05
3,677	Detecting Change in Data Streams	2004	VLDB	7.1049861e-05
3,812	Incremental Record Linkage	2014	VLDB	7.0085181e-05
5,046	Dissecting, Designing, and Optimizing LSM-based Data Stores	2022	SIGMOD	6.3000825e-05
6,428	Domain-Aware Multi-Truth Discovery from Conflicting Sources	2018	VLDB	5.7886234e-05
7,260	Entity Resolution On-Demand	2022	VLDB	5.571581e-05
7,401	BrewER: Entity Resolution On-Demand	2023	VLDB	5.5359939e-05
9,912	FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data	2024	VLDB	5.1103839e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	3,812	Incremental Record Linkage	2014	VLDB
2	5,272	InferDB: In-Database Machine Learning Inference Using Indexes	2024	VLDB
3	10,830	Bridging the Indexing Gap in Fused GPU Query Engines	2026	VLDB
4	12,145	Query-Driven Learning for Next Generation Predictive Modeling & Analytics	2019	SIGMOD
5	3,577	On-the-Fly Entity-Aware Query Processing in the Presence of Linkage	2010	VLDB
6	5,145	Conjunctive Queries with Inequalities Under Updates	2018	VLDB
7	11,598	Searching Data Lakes for Nested and Joined Data	2024	VLDB
8	8,131	Thrifty Query Execution via Incrementability	2020	SIGMOD
9	5,348	Adaptive and Robust Query Execution for Lakehouses at Scale	2024	VLDB
10	9,912	FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data	2024	VLDB