Preprocessing Uncertain Data into Supersequences for Sorting and Gaps

TL;DR

This paper introduces using supersequences as auxiliary structures in preprocessing uncertain data, enabling efficient reconstruction of sorted order and gaps in numerical sets, and offers a simpler alternative to complex structures.

Contribution

It proposes supersequences for preprocessing uncertain data, simplifying auxiliary structures and decoupling preprocessing from reconstruction in sorting and gap problems.

Findings

Supersequences effectively encode sorted order and gaps.

Decoupling preprocessing and reconstruction enables flexible algorithms.

Supersequences are simpler than previous specialized structures.

Abstract

In the preprocessing framework for dealing with uncertain data, one is given a set of regions that one is allowed to preprocess to create some auxiliary structure such that when a realization of these regions is given, consisting of one point per region, this auxiliary structure can be used to reconstruct some desired output structure more efficiently than would have been possible without preprocessing. The framework has been successfully applied to several, mostly geometric, computational problems. In this note, we propose using a supersequence of input items as the auxiliary structure, and explore its potential on the problems of sorting and computing the smallest or largest gap in a set of numbers. That is, our uncertainty regions are intervals on the real line, and in the preprocessing phase we output a supersequence of the intervals such that the sorted order / smallest gap / largest gap of any realization is a subsequence of this sequence. We argue that supersequences are simpler than specialized auxiliary structures developed in previous work. An advantage of using supersequences as the auxiliary structures is that it allows us to decouple the preprocessing phase from the reconstruction phase in a stronger sense than was possible in previous work, resulting in two separate algorithmic problems for which different solutions may be combined to obtain known and new results. We identify one key open problem which we believe is of independent interest.