多重权集上估计总体的协调加权抽样
Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments
摘要：
许多数据源都是通过一组键上的多个权重分配来自然建模的：多个时间点上不断发展的数据库的快照，多个时间段内收集的测量值，对多个位置服务的资源的请求，以及具有多个数值属性的记录。在这样的向量加权数据上，我们感兴趣的是关于一组权重的聚合，例如加权和，以及多组权重的聚合，例如$L_1$差。基于样本的摘要对于太大而无法存储或操作的数据集非常有效。摘要便于近似处理可能在生成摘要之后指定的查询。然而，当前的设计适用于单个{ \em标量}权重与每个键关联的数据集。我们开发了一个抽样框架的基础上{ \em协调加权样本}，适合于多个权重分配，并获得估计，{ \em数量级更紧}比以前可能的。我们通过对从IP网络到股票报价数据的各种数据集进行广泛的实证评估，展示了我们方法的强大功能。
英文摘要：
Many data sources are naturally modeled by multiple weight assignments over a set of keys: snapshots of an evolving database at multiple points in time, measurements collected over multiple time periods, requests for resources served at multiple locations, and records with multiple numeric attributes. Over such vector-weighted data we are interested in aggregates with respect to one set of weights, such as weighted sums, and aggregates over multiple sets of weights such as the $L_1$ difference. Sample-based summarization is highly effective for data sets that are too large to be stored or manipulated. The summary facilitates approximate processing queries that may be specified after the summary was generated. Current designs, however, are geared for data sets where a single {\em scalar} weight is associated with each key. We develop a sampling framework based on {\em coordinated weighted samples} that is suited for multiple weight assignments and obtain estimators that are {\em orders of magnitude tighter} than previously possible. We demonstrate the power of our methods through an extensive empirical evaluation on diverse data sets ranging from IP network to stock quotes data.
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。
