We present aggregate query sculpting (AQS), a faceted visual query technique for large-scale multidimensional data. As a "born scalable" query technique, AQS starts visualization with a single visual mark representing an aggregation of the entire dataset. The user can then progressively explore the dataset through a sequence of operations abbreviated as P6: pivot (facet an aggregate based on an attribute), partition (lay out a facet in space), peek (see inside a subset using an aggregate visual representation), pile (merge two or more subsets), project (extracting a subset into a new substrate), and prune (discard an aggregate not currently of interest). We validate AQS with Dataopsy, a prototype implementation of AQS that has been designed for fluid interaction on desktop and touch-based mobile devices. We demonstrate AQS and Dataopsy using two case studies and three application examples.

我们提出了一种适用于大规模多维数据的聚合查询雕刻（AQS）的分面视觉查询技术。AQS是一种“天生可横向扩展”的查询技术，它通过一个代表整个数据集的单个可视标记开始可视化。用户可以通过P6操作序列逐步探索数据集，包括旋转（基于属性进行聚合分面）、分隔（在空间中布置分面）、窥视（使用聚合可视化表示查看子集的内部）、堆叠（合并两个或多个子集）、项目（将子集提取到新基质中）和修剪（丢弃当前不感兴趣的聚合）。我们借助Dataopsy验证了AQS的可行性，Dataopsy是一个专为桌面和基于触摸的移动设备上流畅交互而设计的AQS原型实现。我们通过两个案例研究和三个应用示例展示了AQS和Dataopsy。

Dataopsy：使用聚合查询塑造的可扩展和流畅的可视化探索