Given a region qR and a future timestamp qT, a "range aggregate" query estimates the number of objects expected to appear in qR at time qT. Currently the only methods for processing such queries are based on spatio-temporal histograms, which have several serious problems. First, they consume considerable space in order to provide accurate estimation. Second, they incur high evaluation cost. Third, their efficiency continuously deteriorates with time. Fourth, their maintenance requires significant update overhead. Motivated by this, we develop Venn sampling (VS), a novel estimation method optimized for a set of "pivot queries" that reflect the distribution of actual ones. In particular, given m pivot queries, VS achieves perfect estimation with only O(m) samples, as opposed to O(2m) required by the current state of the art in workload-aware sampling. Compared with histograms, our technique is much more accurate (given the same space), produces estimates with negligible cost, and does not deteriorate with time. Furthermore, it permits the development of a novel "query-driven" update policy, which reduces the update cost of conventional policies significantly.
展开▼
机译：给定区域q R 和将来的时间戳q T ，“范围汇总”查询会估计预计将出现在q R 中的对象数在时间q T 。当前，处理此类查询的唯一方法是基于时空直方图，这有几个严重的问题。首先，它们消耗大量空间以提供准确的估计。其次，它们产生了高昂的评估成本。第三，随着时间的流逝，它们的效率不断下降。第四，它们的维护需要大量的更新开销。因此，我们开发了Venn采样（VS），这是一种新颖的估计方法，针对一组反映实际查询分布的“枢轴查询”进行了优化。特别地，给定m个枢纽查询，VS仅使用O（m）个样本即可实现完美的估计，与工作负荷感知采样中当前技术水平所需的O（2 m ）相反。与直方图相比，我们的技术更加精确（给定相同的空间），产生的估算成本可忽略不计，并且不会随时间恶化。此外，它允许开发新颖的“查询驱动”更新策略，这大大降低了常规策略的更新成本。
展开▼
