DBScholar

Back to papers

Geometric Approaches for Top-k Queries

Summary: Geometric framing of top-k queries; geometric variants and practical extensions via computational geometry tools. Examines dimensionality effects on meaningfulness; parallels to nearest-neighbor search with implications for recsys and decision-support. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11691
Venue
VLDB
Year
2017
Pagerank
5.3884685e-05
Overall Rank
8,665 | 40.56%
DOI
10.14778/3137765.3137826

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{mouratidis_vldb17,
        title = {{Geometric Approaches for Top-k Queries}},
        author = {Mouratidis, Kyriakos},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {12},
        pages = {1985},
        doi = {10.14778/3137765.3137826},
        url = {https://doi.org/10.14778/3137765.3137826},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,465 On Obtaining Stable Rankings 2019 VLDB 6.2075408e-05
5,601 Optimal Join Algorithms Meet Top-k 2020 SIGMOD 6.1540123e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 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