Sheaves are the canonical datastructure for sensor integration

TL;DR

This paper advocates for using sheaves as a universal data structure for sensor integration, highlighting their ability to accurately represent diverse sources and summarize actionable information effectively.

Contribution

It demonstrates that sheaves are the canonical framework for sensor integration, providing mathematical tools that surpass other representations in expressiveness and inferential capabilities.

Findings

Sheaves can represent all information sources accurately.

Sheaves facilitate faithful summarization of information.

Mathematics of sheaves offers powerful inferential tools.

Abstract

A sensor integration framework should be sufficiently general to accurately represent all information sources, and also be able to summarize information in a faithful way that emphasizes important, actionable information. Few approaches adequately address these two discordant requirements. The purpose of this expository paper is to explain why sheaves are the canonical data structure for sensor integration and how the mathematics of sheaves satisfies our two requirements. We outline some of the powerful inferential tools that are not available to other representational frameworks.