# Preprocessing Ambiguous Imprecise Points

## TL;DR

This paper introduces the ambiguity measure for uncertain regions and demonstrates efficient preprocessing for sorting and quadtree reconstruction, with bounds related to ambiguity and interval entropy.

## Contribution

It defines the ambiguity measure as a finer overlap metric and shows how to preprocess regions to enable fast reconstruction based on this measure.

## Key findings

- Preprocessing of disks in $O(n \,\log n)$ time enables fast reconstruction.
- Ambiguity $A(R)$ bounds the reconstruction time and can be efficiently computed.
- In 1D, ambiguity relates to interval entropy and approximates graph entropy for interval orders.

## Abstract

Let ${R} = \{R_1, R_2, ..., R_n\}$ be a set of regions and let $ X = \{x_1, x_2, ..., x_n\}$ be an (unknown) point set with $x_i \in R_i$. Region $R_i$ represents the uncertainty region of $x_i$. We consider the following question: how fast can we establish order if we are allowed to preprocess the regions in $R$? The preprocessing model of uncertainty uses two consecutive phases: a preprocessing phase which has access only to ${R}$ followed by a reconstruction phase during which a desired structure on $X$ is computed. Recent results in this model parametrize the reconstruction time by the ply of ${R}$, which is the maximum overlap between the regions in ${R}$. We introduce the ambiguity $A({R})$ as a more fine-grained measure of the degree of overlap in ${R}$. We show how to preprocess a set of $d$-dimensional disks in $O(n \log n)$ time such that we can sort $X$ (if $d=1$) and reconstruct a quadtree on $X$ (if $d\geq 1$ but constant) in $O(A({R}))$ time. If $A({R})$ is sub-linear, then reporting the result dominates the running time of the reconstruction phase. However, we can still return a suitable data structure representing the result in $O(A({R}))$ time. In one dimension, ${R}$ is a set of intervals and the ambiguity is linked to interval entropy, which in turn relates to the well-studied problem of sorting under partial information. The number of comparisons necessary to find the linear order underlying a poset $P$ is lower-bounded by the graph entropy of $P$. We show that if $P$ is an interval order, then the ambiguity provides a constant-factor approximation of the graph entropy. This gives a lower bound of $\Omega(A({R}))$ in all dimensions for the reconstruction phase (sorting or any proximity structure), independent of any preprocessing; hence our result is tight.