The analytics of spatiotemporal data is increasingly important for mobility analytics. Despite extensive research on moving object databases (MODs), few systems are ready on production or lightweight enough for analytics. MobilityDB is a notable system that extends PostgreSQL with spatiotemporal data, but it inherits complexity of the architecture as well. In this paper, we present MobilityDuck, a DuckDB extension that integrates the MEOS library to provide support spatiotemporal and other temporal data types in DuckDB. MobilityDuck leverages DuckDB's lightweight, columnar, in-memory executable properties to deliver efficient analytics. To the best of our knowledge, no existing in-memory or embedded analytical system offers native spatiotemporal types and continuous trajectory operators as MobilityDuck does. We evaluate MobilityDuck using the BerlinMOD-Hanoi benchmark dataset and compare its performance to MobilityDB. Our results show that MobilityDuck preserves the expressiveness of spatiotemporal queries while benefiting from DuckDB's in-memory, columnar architecture.
翻译:时空数据分析在移动性分析中日益重要。尽管移动对象数据库(MODs)已得到广泛研究,但鲜有系统能够实际投入生产环境或足够轻量以支持分析任务。MobilityDB是一个显著的系统,它在PostgreSQL中扩展了时空数据功能,但也继承了其架构的复杂性。本文提出MobilityDuck——一个集成MEOS库的DuckDB扩展,为DuckDB提供时空及其他时序数据类型的支持。MobilityDuck利用DuckDB轻量级、列式、内存可执行的特性,实现了高效的分析功能。据我们所知,目前尚无其他内存或嵌入式分析系统能像MobilityDuck这样提供原生的时空数据类型与连续轨迹操作符。我们使用BerlinMOD-Hanoi基准数据集对MobilityDuck进行评估,并将其性能与MobilityDB进行对比。实验结果表明,MobilityDuck在保持时空查询表达力的同时,充分发挥了DuckDB内存列式架构的优势。