Spatial joins in main memory

Abstract

A recent PVLDB paper reports on experimental analyses of ten spatial join techniques in main memory. We build on this comprehensive study to raise awareness of the fact that empirical running time performance findings in main-memory settings are results of not only the algorithms and data structures employed, but also their implementation, which complicates the interpretation of the results.In particular, we re-implement the worst performing technique without changing the underlying high-level algorithm, and we then offer evidence that the resulting re-implementation is capable of outperforming all the other techniques. This study demonstrates that in main memory, where no time-consuming I/O can mask variations in implementation, implementation details are very important; and it offers a concrete illustration of how it is difficult to make conclusions from empirical running time performance findings in main-memory settings about data structures and algorithms studied.

Thermal-Join

“…We do not use the available implementations provided in [34] because the code assumes two-dimensional spatial datasets and extending it to support the three-dimensional dataset we use is not trivial [37]. The implementations are all written in C++.…”

“…Recent analyses [34,37] have demonstrated the limitations of existing join techniques for the problem of moving objects. Furthermore, known approaches do not scale in case scientists increase the precision of the simulation application.…”