The Case for Distance-Bounded Spatial Approximations

TL;DR

This paper advocates for using distance-bounded spatial approximations to enable fast, approximate query processing in spatial databases, especially suited for applications needing quick responses with acceptable accuracy.

Contribution

It introduces a novel approach for approximate spatial data processing that leverages distance bounds to control accuracy and performance, moving beyond traditional filtering methods.

Findings

Approximate techniques can replace exact geometric tests for faster responses.

Distance bounds effectively control the trade-off between accuracy and performance.

Hardware advances make real-time approximate spatial processing feasible.

Abstract

Spatial approximations have been traditionally used in spatial databases to accelerate the processing of complex geometric operations. However, approximations are typically only used in a first filtering step to determine a set of candidate spatial objects that may fulfill the query condition. To provide accurate results, the exact geometries of the candidate objects are tested against the query condition, which is typically an expensive operation. Nevertheless, many emerging applications (e.g., visualization tools) require interactive responses, while only needing approximate results. Besides, real-world geospatial data is inherently imprecise, which makes exact data processing unnecessary. Given the uncertainty associated with spatial data and the relaxed precision requirements of many applications, this vision paper advocates for approximate spatial data processing techniques that omit exact geometric tests and provide final answers solely on the basis of (fine-grained) approximations. Thanks to recent hardware advances, this vision can be realized today. Furthermore, our approximate techniques employ a distance-based error bound, i.e., a bound on the maximum spatial distance between false (or missing) and exact results which is crucial for meaningful analyses. This bound allows to control the precision of the approximation and trade accuracy for performance.