The advancement of mobile computing devices and positioning technologies has led to an explosive growth of spatio-temporal data managed in databases. Representative queries over such data include range queries, nearest neighbor queries, and join queries. However, formulating those queries usually requires domain-specific expertise and familiarity with executable query languages, which would be a challenging task for non-expert users. It leads to a great demand for well-supported natural language queries (NLQs) in spatio-temporal databases. To bridge the gap between non-experts and query plans in databases, we present NL4ST, an interactive tool that allows users to query spatio-temporal databases in natural language. NL4ST features a three-layer architecture: (i) knowledge base and corpus for knowledge preparation, (ii) natural language understanding for entity linking, and (iii) generating physical plans. Our demonstration will showcase how NL4ST provides effective spatio-temporal physical plans, verified by using four real and synthetic datasets. We make NL4ST online and provide the demo video at https://youtu.be/-J1R7R5WoqQ.

翻译：移动计算设备与定位技术的进步导致数据库中管理的时空数据呈爆炸式增长。对此类数据的典型查询包括范围查询、最近邻查询和连接查询。然而，构建这些查询通常需要领域专业知识并熟悉可执行查询语言，这对非专业用户而言是一项具有挑战性的任务。这导致时空数据库对良好支持的自然语言查询（NLQ）产生了巨大需求。为弥合非专业用户与数据库查询计划之间的鸿沟，我们提出了NL4ST——一个允许用户以自然语言查询时空数据库的交互式工具。NL4ST采用三层架构：（i）用于知识准备的知识库与语料库，（ii）用于实体链接的自然语言理解模块，以及（iii）物理计划生成模块。我们的演示将展示NL4ST如何通过四个真实与合成数据集验证，生成有效的时空物理查询计划。NL4ST已在线部署，演示视频详见 https://youtu.be/-J1R7R5WoqQ。