Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such queries can be challenging due to the fuzzy nature of real-world data; in particular, they often include real-valued parameters that must be tuned by hand. We propose a novel framework called Quivr that synthesizes trajectory queries matching a given set of examples. To efficiently synthesize parameters, we introduce a novel technique for pruning the parameter space and a novel quantitative semantics that makes this more efficient. We evaluate Quivr on a benchmark of 17 tasks, including several from prior work, and show both that it can synthesize accurate queries for each task and that our optimizations substantially reduce synthesis time.

翻译：数据科学家常需编写程序来处理机器学习模型的预测结果，例如视频数据中的目标检测与轨迹。然而，由于现实世界数据的模糊性，编写此类查询具有挑战性；特别是，这些查询通常包含需要手动调整的实值参数。我们提出了一种名为Quivr的新型框架，能够合成与给定示例集匹配的轨迹查询。为高效合成参数，我们引入了一种创新的参数空间剪枝技术，以及一种提升效率的新型定量语义方法。我们在包含17项任务的基准测试（其中多项源自先前研究）中对Quivr进行评估，结果表明：该框架不仅能为每项任务合成精确查询，且我们的优化方案显著缩短了合成时间。