Database Paper Browser

Back to papers

Collective Grounding: Applying Database Techniques to Grounding Templated Models

Summary: Collective grounding: treat grounding of templated relational models as a joint, interdependent workload to enable shared computation using DB techniques (query planning, join/provenance) to cut grounding cost. Implements components and shows up to 70% runtime reduction on seven datasets; useful for relational learning and non-independent probabilistic DBs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13041
Venue
VLDB
Year
2023
Pagerank
4.1945683e-05
Overall Rank
11,232 | 21.87%
DOI
10.14778/3594512.3594516

Incoming Non-self Citations Over Time

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

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers