Demo Data from VLDB 2026 / reviewers in the wild / expert
Connor Colombe
dblp:270/2218
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Computational geometry · 50% Approximation and online algorithms · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Approximation and online algorithms
approximation algorithms |
0.5 | 1 | 2021 | Approximating the (Continuous) Fréchet Distance · SoCG 2021 |
Computational geometry › curve similarity
fréchet distance |
0.5 | 1 | 2021 | Approximating the (Continuous) Fréchet Distance · SoCG 2021 |
Computational geometry
shape analysis |
0.5 | 1 | 2021 | Approximating the (Continuous) Fréchet Distance · SoCG 2021 |
Approximation and online algorithms › approximation algorithms
subquadratic approximation |
0.5 | 1 | 2021 | Approximating the (Continuous) Fréchet Distance · SoCG 2021 |
Methods — techniques the papers use, named apart from their topics
decision-to-optimization reduction · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Approximating the (Continuous) Fréchet DistanceabstractWe describe the first strongly subquadratic time algorithm with subexponential approximation ratio for approximately computing the Fréchet distance between two polygonal chains. Specifically, let $P$ and $Q$ be two polygonal chains with $n$ vertices in $d$-dimensional Euclidean space, and let $α\in [\sqrt{n}, n]$. Our algorithm deterministically finds an $O(α)$-approximate Fréchet correspondence in time $O((n^3 / α^2) \log n)$. In particular, we get an $O(n)$-approximation in near-linear $O(n \log n)$ time, a vast improvement over the previously best know result, a linear time $2^{O(n)}$-approximation. As part of our algorithm, we also describe how to turn any approximate decision procedure for the Fréchet distance into an approximate optimization algorithm whose approximation ratio is the same up to arbitrarily small constant factors. The transformation into an approximate optimization algorithm increases the running time of the decision procedure by only an $O(\log n)$ factor. Connor Colombe, Kyle Fox |
SoCG | 1 |