Spatial Queries with Two kNN Predicates

TL;DR

This paper introduces optimized algorithms for complex spatial queries involving two kNN predicates, significantly improving execution efficiency over traditional methods.

Contribution

It extends kNN query optimization to scenarios with two kNN predicates, providing new algorithms and evaluation plans for such complex queries.

Findings

Algorithms outperform traditional plans by orders of magnitude

Optimizations apply to various combinations of kNN-select and kNN-join queries

Experimental results validate the efficiency of proposed methods

Abstract

The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimization of queries with single kNN predicates, and shows how queries with two kNN predicates can be optimized. In particular, the paper addresses the optimization of queries with: (i) two kNN-select predicates, (ii) two kNN-join predicates, and (iii) one kNN-join predicate and one kNN-select predicate. For each type of queries, conceptually correct query evaluation plans (QEPs) and new algorithms that optimize the query execution time are presented. Experimental results demonstrate that the proposed algorithms outperform the conceptually correct QEPs by orders of magnitude.