Spatiotemporal Data Warehouse for Vehicle Supervision

Abstract

The large amounts of spatiotemporal data generated by vehicle supervision systems cannot be efficiently managed by ordinary databases, mainly because of long query responses. To overcome the limitations of ordinary databases, this paper proposes a new approach, the grid time (GT)–indexed cube, which is a spatial grid–indexed, adaptive grid-based, trajectory-supported warehouse for spatiotemporal data. The GT cube partitions an embedded space–time into a set of size-fixed grids to form a cube that continues to grow throughout a constant time interval. Each grid is assigned an identifier composed of its coordinates and start time, and an aggregated value for each grid is stored in the grid records, regardless of the temporal length of the queries. Additionally, the basic grid structure of the GT cube remains unchanged at each time interval. Instead, this method refines the grid in a selected region to handle data skewing by adaptively partitioning the grid into subgrids. After extensive performance studies were conducted with spatiotemporal data from the main vehicle supervision system of Guangdong Province, China, it was observed that the GT cube achieved higher query performance than ordinary data storage technologies under various operational conditions, was easily applicable in practice, and demonstrated compatibility with traditional databases.