To enable the obstacle-detection use case, in which vehicles approaching an obstacle (e.g., falling object) on the road are immediately notified of the obstacle, and the lane-specific congestion detection use case, in which the number of vehicles in each lane on the road is determined, it is necessary to store data sent simultaneously from a large number of connected vehicles, search the data in real time for vehicles present within a certain area (mesh area, road, parking lot, etc.) at any specific time, and determine the number of these vehicles. This article describes the real-time spatiotemporal data-management technology (Axispot TM ) that we are developing to meet these requirements.

We introduce the Axispot TM real-time spatio-temporal data-management system, which is a key component in responding to the demands for next-generation services such as communication between connected vehicles and augmented reality. We also describe a high-speed spatio-temporal data-search technology as a key function of Axispot, which can not only accumulate a large amount of data sent all at once from moving things (MTs), such as people or automobiles, but also search for MTs in a particular area and at a certain time from a large amount of data stored in a database in real time.

No abstract