Tight Bounds for Approximate Near Neighbor Searching for Time Series under the Fr\'echet Distance

TL;DR

This paper develops new data structures for approximate near neighbor search in time series under the Fréchet distance, achieving improved approximation factors and query times with tight bounds based on the Orthogonal Vectors Hypothesis.

Contribution

It introduces novel upper bounds and matching lower bounds for approximate near neighbor search in one-dimensional time series under the Fréchet distance, with improved tradeoffs.

Findings

Achieves $(1+ ext{epsilon})$ approximation with same time as previous $(2+ ext{epsilon})$ result.

Provides linear preprocessing and space with query time depending on epsilon and k.

Establishes conditional lower bounds based on the Orthogonal Vectors Hypothesis.

Abstract

We study the $c$-approximate near neighbor problem under the continuous Fr\'echet 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 that, given a query curve $q$ with $k$ vertices, either returns an input curve with Fr\'echet distance at most $c\cdot \delta$ to $q$, or returns that there exists no input curve with Fr\'echet distance at most $\delta$ to $q$. We focus on the case where the input and the queries are one-dimensional polygonal curves -- also called time series -- and we give a comprehensive analysis for this case. We obtain new upper bounds that provide different tradeoffs between approximation factor, preprocessing time, and query time. Our data structures improve upon the state of the art in several ways. We show that for any $0 < \varepsilon \leq 1$ an approximation factor of $(1+\varepsilon)$ can be achieved within the same asymptotic time bounds as the previously best result for $(2+\varepsilon)$. Moreover, we show that an approximation factor of $(2+\varepsilon)$ can be obtained by using preprocessing time and space $O(nm)$, which is linear in the input size, and query time in $O(\frac{1}{\varepsilon})^{k+2}$, where the previously best result used preprocessing time in $n \cdot O(\frac{m}{\varepsilon k})^k$ and query time in $O(1)^k$. We complement our upper bounds with matching conditional lower bounds based on the Orthogonal Vectors Hypothesis. Interestingly, some of our lower bounds already hold for any super-constant value of $k$. This is achieved by proving hardness of a one-sided sparse version of the Orthogonal Vectors problem as an intermediate problem, which we believe to be of independent interest.