论文标题
LAQP：基于学习的近似查询处理
LAQP: Learning-based Approximate Query Processing
论文作者
论文摘要
由于数据量的快速增长，大数据查询是一项具有挑战性的任务。近似查询处理（AQP）是满足快速响应需求的一种方法。在本文中，我们提出了一种基于学习的AQP方法，称为LAQP。 LAQP构建了从历史查询中学到的错误模型，以预测每个新查询的基于采样的估计错误。它结合了基于采样的AQP，预计的聚合和学习的错误模型，以提供高精确的查询估计，并使用一个小的离线样本。实验结果表明，我们的LAQP优于基于采样的AQP，基于聚集的AQP和最新的基于学习的AQP方法。
Querying on big data is a challenging task due to the rapid growth of data amount. Approximate query processing (AQP) is a way to meet the requirement of fast response. In this paper, we propose a learning-based AQP method called the LAQP. The LAQP builds an error model learned from the historical queries to predict the sampling-based estimation error of each new query. It makes a combination of the sampling-based AQP, the pre-computed aggregations and the learned error model to provide high-accurate query estimations with a small off-line sample. The experimental results indicate that our LAQP outperforms the sampling-based AQP, the pre-aggregation-based AQP and the most recent learning-based AQP method.
