具有分布敏感区间保证的快速近似集结
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees
摘要：
聚合数据是数据分析、数据探索和OLAP的基础。近似查询处理（AQP）技术通常用于加速使用样本的聚集计算，其中置信区间（CI）被广泛用于量化相关误差。实践中使用的CI分为两类：严格但不正确的技术，即，它们产生紧密的间隔，但仅提供渐近保证，使得它们不可靠，或者是正确但不紧密的技术，即，它们提供了严格的保证，但过于保守，导致置信区间过于宽松而无法使用。在本文中，我们开发了一种CI技术，这是正确的和更严格的比传统的方法。从保守的CI开始，我们确定了他们经常面临的两个问题：悲观质量分配（PMA）和幻影离群值敏感性（PHOS）。通过开发一种新的范围修剪技术，消除PHOS和配对它与已知的CI技术没有PMA，我们开发了一种技术，用于计算CI具有较强的保证，需要较少的样本相同的宽度。我们在采样优化的内存列存储下实现了我们的技术，并展示了如何加速涉及真实数据集上的聚合的查询，其速度比传统的AQP保证方法提高了高达124倍，比精确方法提高了1000倍以上。
英文摘要：
Aggregating data is fundamental to data analytics, data exploration, and OLAP. Approximate query processing (AQP) techniques are often used to accelerate computation of aggregates using samples, for which confidence intervals (CIs) are widely used to quantify the associated error. CIs used in practice fall into two categories: techniques that are tight but not correct, i.e., they yield tight intervals but only offer asymptotic guarantees, making them unreliable, or techniques that are correct but not tight, i.e., they offer rigorous guarantees, but are overly conservative, leading to confidence intervals that are too loose to be useful. In this paper, we develop a CI technique that is both correct and tighter than traditional approaches. Starting from conservative CIs, we identify two issues they often face: pessimistic mass allocation (PMA) and phantom outlier sensitivity (PHOS). By developing a novel range-trimming technique for eliminating PHOS and pairing it with known CI techniques without PMA, we develop a technique for computing CIs with strong guarantees that requires fewer samples for the same width. We implement our techniques underneath a sampling-optimized in-memory column store and show how to accelerate queries involving aggregates on a real dataset with speedups of up to 124x over traditional AQP-with-guarantees and more than 1000x over exact methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
