3 research outputs found
Adaptive geospatial joins for modern hardware
Geospatial joins are a core building block of connected
mobility applications. An especially challenging problem
are joins between streaming points and static polygons. Since
points are not known beforehand, they cannot be indexed.
Nevertheless, points need to be mapped to polygons with low
latencies to enable real-time feedback.
We present an adaptive geospatial join that uses true hit
filtering to avoid expensive geometric computations in most
cases. Our technique uses a quadtree-based hierarchical grid
to approximate polygons and stores these approximations in a
specialized radix tree. We emphasize on an approximate version
of our algorithm that guarantees a user-defined precision. The
exact version of our algorithm can adapt to the expected point
distribution by refining the index. We optimized our implementation
for modern hardware architectures with wide SIMD vector
processing units, including Intel’s brand new Knights Landing.
Overall, our approach can perform up to two orders of magnitude
faster than existing techniques
Approximate geospatial joins with precision guarantees
Geospatial joins are a core building block of con-
nected mobility applications. An especially challenging problem
are joins between streaming points and static polygons. Since
points are not known beforehand, they cannot be indexed.
Nevertheless, points need to be mapped to polygons with low
latencies to enable real-time feedback.
We present an approximate geospatial join that guarantees
a user-defined precision. Our technique uses a quadtree-based
hierarchical grid to approximate polygons and stores these
approximations in a specialized radix tree. Our approach can
perform up to several orders of magnitude faster than existing
techniques while providing sufficiently precise results for many
applications
Adaptive main-memory indexing for high-performance point-polygon joins
Connected mobility applications rely heavily on geospatial joins that associate point data, such as locations of Uber cars, to static polygonal regions, such as city neighborhoods. These joins typically involve expensive geometric computations, which makes it hard to provide an interactive user experience. In this paper, we propose an adaptive polygon index that leverages true hit fltering to avoid expensive geometric computations in most cases. In particular, our approach closely approximates polygons by combining quadtrees with true hit filtering, and stores these approximations in a query-effcient radix tree. Based on this index, we introduce two geospatial join algorithms: an approximate one that guarantees a user-defined precision, and an exact one that adapts to the expected point distribution. In summary, our technique outperforms existing CPU-based joins by up to two orders of magnitude and is competitive with state-of-the-art GPU implementations