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)
Incoming Non-self Citations Over Time
Authors
- 1. Kyriakos Mouratidis (Singapore Management University)
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 |
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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.
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| 1 | 5,601 | Optimal Join Algorithms Meet Top-k | 2020 | SIGMOD |
| 2 | 1,605 | Efficient Search for the Top-k Probable Nearest Neighbors in Uncertain Databases | 2008 | VLDB |
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| 4 | 1,441 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB |
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| 6 | 7,299 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD |
| 7 | 8,281 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB |
| 8 | 2,827 | Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data | 2007 | VLDB |
| 9 | 3,310 | Similarity Query Processing for High-Dimensional Data | 2020 | VLDB |
| 10 | 12,331 | Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions | 2012 | VLDB |