Evaluating Methods for Efficient Entity Count Estimation

Summary: Formalizes entity-count estimation for queries involving entity resolution, decomposing the task into a modular pipeline and unifying statistical and DB perspectives. Systematically maps the design space and evaluates representative pipelines on real benchmarks, exposing method-specific trade-offs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14091
Venue: VLDB
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,878 | 25.37%
DOI: 10.14778/3742728.3742750

Incoming Non-self Citations Over Time

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

Authors

1. Jerin George Mathew (Sapienza Università di Roma)
2. Donatella Firmani (Sapienza Università di Roma)
3. Divesh Srivastava (AT&T)

BibTeX Citation

@article{mathew_vldb25,
        title = {{Evaluating Methods for Efficient Entity Count Estimation}},
        author = {Mathew, Jerin George and Firmani, Donatella and Srivastava, Divesh},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {8},
        pages = {2589--2601},
        doi = {10.14778/3742728.3742750},
        url = {https://doi.org/10.14778/3742728.3742750},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
31	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00050347119
89	On the Propagation of Errors in the Size of Join Results	1991	SIGMOD	0.00035031529
141	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB	0.0002964847
176	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00027191081
248	Evaluation of entity resolution approaches on real-world match problems	2010	VLDB	0.00023278354
291	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00022264197
489	Distributed Representations of Tuples for Entity Resolution	2018	VLDB	0.0001761456
737	Join Size Estimation Subject to Filter Conditions	2015	VLDB	0.00014490983
1,443	Crowdsourcing Algorithms for Entity Resolution	2014	VLDB	0.00010773106
1,664	Two-Level Sampling for Join Size Estimation	2017	SIGMOD	0.00010070362
2,290	ZeroER: Entity Resolution using Zero Labeled Examples	2020	SIGMOD	8.799251e-05
2,475	Deep Learning for Blocking in Entity Matching: A Design Space Exploration	2021	VLDB	8.5277654e-05
3,578	Advanced Join Strategies for Large-Scale Distributed Computation	2014	VLDB	7.2899943e-05
3,602	Online Entity Resolution Using an Oracle	2016	VLDB	7.2691521e-05
4,940	BEER: Blocking for Effective Entity Resolution	2021	SIGMOD	6.4336209e-05
8,592	Robust Entity Resolution using Random Graphs	2018	SIGMOD	5.4058393e-05

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