Spatial Independent Range Sampling

Thanks to the wide adoption of GPS-equipped devices, the volume of collected spatial data is exploding. To achieve interactive exploration and analysis over big spatial data, people are willing to trade off accuracy for performance through approximation. As a foundation in many approximate algorithms, data sampling now requires more flexibility and better performance. In this paper, we study the spatial independent range sampling (SIRS) problem aiming at retrieving random samples with independence over points residing in a query region. Specifically, we have designed concise index structures with careful data layout based on various space decomposition strategies. Moreover, we propose novel algorithms for both uniform and weighted SIRS queries with low theoretical cost and complexity as well as excellent practical performance. Last but not least, we demonstrate how to support data updates and trade-offs between different sampling methods in practice. According to comprehensive evaluations conducted on real-world datasets, our methods achieve orders of magnitude performance improvement against baselines derived by existing works.