The spatial intersection join an important spatial query operation, due to
its popularity and high complexity. The spatial join pipeline takes as input
two collections of spatial objects (e.g., polygons). In the filter step, pairs
of object MBRs that intersect are identified and passed to the refinement step
for verification of the join predicate on the exact object geometries. The
bottleneck of spatial join evaluation is in the refinement step. We introduce
APRIL, a powerful intermediate step in the pipeline, which is based on raster
interval approximations of object geometries. Our technique applies a sequence
of interval joins on 'intervalized' object approximations to determine whether
the objects intersect or not. Compared to previous work, APRIL approximations
are simpler, occupy much less space, and achieve similar pruning effectiveness
at a much higher speed. Besides intersection joins between polygons, APRIL can
directly be applied and has high effectiveness for polygonal range queries,
within joins, and polygon-linestring joins. By applying a lightweight
compression technique, APRIL approximations may occupy even less space than
object MBRs. Furthermore, APRIL can be customized to apply on partitioned data
and on polygons of varying sizes, rasterized at different granularities. Our
last contribution is a novel algorithm that computes the APRIL approximation of
a polygon without having to rasterize it in full, which is orders of magnitude
faster than the computation of other raster approximations. Experiments on real
data demonstrate the effectiveness and efficiency of APRIL; compared to the
state-of-the-art intermediate filter, APRIL occupies 2x-8x less space, is
3.5x-8.5x more time-efficient, and reduces the end-to-end join cost up to 3
times.Comment: 12 page