The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools. In contrast to previous sampling-based studies, we treat the problem as an optimization problem whose goal is to minimize the error in answering queries in the given workload. A key novelty of our approach is that we can tailor the choice of samples to be robust even for workloads that are "similar" but not necessarily identical to the given workload. Finally, our techniques recognize the importance of taking into account the variance in the data distribution in a principled manner. We show how our solution can be implemented on a database system, and present results of extensive experiments on Microsoft SQL Server 2000 that demonstrate the superior quality of our method compared to previous work.
机译:APPROXIMATE-一种查询处理器,可单调改善近似答案
机译:基于关系模式的P2P数据库系统中基于蚁群优化的距离查询回答算法
机译:使用动态前缀聚合树回答数据流上的临时连续聚合查询
机译:一种强大的,基于优化的方法,用于汇总查询的近似答案
机译:关系数据库中聚合查询的近似答案。
机译:QAGView:交互式汇总高价值的汇总查询答案
机译:一种强大的,基于优化的方法,用于近似回答聚合查询
机译:生成数据库查询的近似答案