  Geospatial data constitutes a considerable part of (Semantic) Web data, but
so far, its sources are inadequately interlinked in the Linked Open Data cloud.
Geospatial Interlinking aims to cover this gap by associating geometries with
topological relations like those of the Dimensionally Extended 9-Intersection
Model. Due to its quadratic time complexity, various algorithms aim to carry
out Geospatial Interlinking efficiently. We present JedAI-spatial, a novel,
open-source system that organizes these algorithms according to three
dimensions: (i) Space Tiling, which determines the approach that reduces the
search space, (ii) Budget-awareness, which distinguishes interlinking
algorithms into batch and progressive ones, and (iii) Execution mode, which
discerns between serial algorithms, running on a single CPU-core, and parallel
ones, running on top of Apache Spark. We analytically describe JedAI-spatial's
architecture and capabilities and perform thorough experiments to provide
interesting insights about the relative performance of its algorithms.
