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The Frechet distance is a popular similarity measure between curves. For some applications, it is desirable to match the curves under translation before computing the Frechet distance between them. This variant is called the Translation Invariant Frechet distance, and algorithms to compute it are well studied. The query version, finding an optimal placement in the plane for a query segment where the Frechet distance becomes minimized, is much less well understood. We study Translation Invariant Frechet distance queries in a restricted setting of horizontal query segments. More specifically, we preprocess a trajectory in $mathcal O(n^2 log^2 n) $ time and $mathcal O(n^{3/2})$ space, such that for any subtrajectory and any horizontal query segment we can compute their Translation Invariant Frechet distance in $mathcal O(text{polylog } n)$ time. We hope this will be a step towards answering Translation Invariant Frechet queries between arbitrary trajectories.
We study metric data structures for curves in doubling spaces, such as trajectories of moving objects in Euclidean $mathbb{R}^d$, where the distance between two curves is measured using the discrete Frechet distance. We design data structures in an e
In this paper we study a wide range of variants for computing the (discrete and continuous) Frechet distance between uncertain curves. We define an uncertain curve as a sequence of uncertainty regions, where each region is a disk, a line segment, or
The Frechet distance is a popular distance measure for curves which naturally lends itself to fundamental computational tasks, such as clustering, nearest-neighbor searching, and spherical range searching in the corresponding metric space. However, i
We study approximate-near-neighbor data structures for time series under the continuous Frechet distance. For an attainable approximation factor $c>1$ and a query radius $r$, an approximate-near-neighbor data structure can be used to preprocess $n$ c
We study the $c$-approximate near neighbor problem under the continuous Frechet distance: Given a set of $n$ polygonal curves with $m$ vertices, a radius $delta > 0$, and a parameter $k leq m$, we want to preprocess the curves into a data structure t