Learning Efficiently Over Heterogeneous Databases

Summary: CastorX extends relational learning to heterogeneous databases with matching dependencies for schema alignment and similarity-based matches. Sampling sharpens learning efficiency while yielding accurate Datalog definitions of the target relation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11900
Venue: VLDB
Year: 2018
Pagerank: 5.093636e-05
Overall Rank: 11,948 | 18.03%
DOI: 10.14778/3229863.3236261

Incoming Non-self Citations Over Time

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

Authors

1. Jose Picado (Oregon State University)
2. Arash Termehchy (Oregon State University)
3. Sudhanshu Pathak (Oregon State University)

BibTeX Citation

@article{picado_vldb18,
        title = {{Learning Efficiently Over Heterogeneous Databases}},
        author = {Picado, Jose and Termehchy, Arash and Pathak, Sudhanshu},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {12},
        pages = {2066--2069},
        doi = {10.14778/3229863.3236261},
        url = {https://doi.org/10.14778/3229863.3236261},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
619	Reasoning about Record Matching Rules	2009	VLDB	0.00015707247
764	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	2016	SIGMOD	0.00014226652
7,985	Schema Independent Relational Learning	2017	SIGMOD	5.5125969e-05

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