The smart city system, which is a type of enterprise management system (EMS), automatically manages cities and schedules resources efficiently based on spatial data generated by devices, such as the Internet of Things and mobile. However, with the increasing deployment of technologies, including sensor and location-based services, their ever-growing spatial data are no longer managed efficiently by traditional EMS. To overcome this issue, we present SeFrame, which is a s patially e nabled frame work for improving the efficiency of smart city EMS based on a distributed architecture. The framework supports a set of spatial queries, including: The range query, k-nearest neighbors query, and spatial join query. It benefits greatly from using the buffer-enabled partition method to eliminate duplicate results. In each partition, the local index based on combination of the quad-tree and grid index (CQG) significantly improves the spatial query efficiency in memory. CQG manages complex spatial objects, including a point, polygon, and polyline. By taking full advantage of the local index, SeFrame accesses skewed spatial data in constant time. In experiments, we demonstrated that the proposed method delivered superior performance in terms of scalability and query efficiency, in most cases.

中文翻译：

---

提高基于 EMS 的智慧城市的效率：一种新型的空间数据分布式框架

智慧城市系统是一种企业管理系统（EMS），基于物联网和移动设备等设备产生的空间数据，自动管理城市并高效调度资源。然而，随着技术部署的增加，包括传感器和基于位置的服务，它们不断增长的空间数据不再由传统的 EMS 有效管理。为了克服这个问题，我们提出了 SeFrame，它是一个耐心地启用基于分布式架构的智慧城市EMS效率提升框架 该框架支持一组空间查询，包括：范围查询、k-最近邻查询和空间连接查询。它极大地受益于使用启用缓冲区的分区方法来消除重复结果。在每个分区中，基于四叉树和网格索引（CQG）结合的局部索引显着提高了内存中的空间查询效率。CQG 管理复杂的空间对象，包括点、多边形和折线。通过充分利用本地索引，SeFrame 在恒定时间内访问倾斜的空间数据。在实验中，我们证明了在大多数情况下，所提出的方法在可扩展性和查询效率方面提供了卓越的性能。