Stream data processing platform from Fujitsu aims to accelerate use of automotive big data
Fujitsu announced it will launch a new stream data processing platform for service providers to maximize the use of big data collected from connected cars. The new platform facilitates simple and efficient automotive big data analysis by leveraging Fujitsu's data processing technology, Dracena, a stream processing architecture that can add or change content while processing large volumes of IoT data, without stopping.
The new platform allows for the management and processing of data in discrete units of people and objects including pedestrians, vehicles, roads, and buildings. This makes it possible to digitally reproduce the surrounding situation, including other vehicles. Continuous data processing additionally offers users the flexibility to add and change services that must operate without disruption, such as real-time hazard prediction for connected cars. Going forward, Fujitsu plans to roll out the service in overseas regions including North America and Europe.
Fujitsu says it is advocating and promoting the digital twin technology for the mobility space, based on the idea of digitally reproducing information about vehicles and roads in real-time. To create a digital twin for use in a mobility context, Fujitsu has launched its new stream data processing platform to support the development of services that leverage automotive big data to contribute to the realization of a safe, secure, and comfortable mobile society.
Fujitsu is offering a data processing platform powered by its Dracena technology. Data and data processing programs (referred to below as plugins) are managed as objects in an in-memory system in stream processing for pedestrians, vehicles, roads, buildings, and other objects from the real-world. Parallel processing, as well as data processing content, can be added and modified in an agile manner while the system is running, offering service providers the ability to flexibly respond to data analysis and prediction services in various use cases, while providing safe, secure, and comfortable mobility services to drivers and carriers on the road. After initial availability in Japan, the solution will subsequently be rolled out in North America and Europe.
By analyzing the driving conditions of each vehicle on a road-to-road basis in real time, the technology creates a virtual simulation of road conditions that delivers users almost instantaneous traffic information about traffic jams and driving hazards. In addition, by analyzing and predicting current, past, and future states while processing the data flowing in continuously and saving several instances worth of the results of data processing in memory, the technology offers services such as driving diagnostics and failure prevention for car batteries. By improving existing services and adding new services without interrupting existing functionality, it becomes possible to support new services like hazard prediction for connected cars and driving assistance, which must operate non-stop.
The new platform consists of essential and miscellaneous optional services including a platform service for managing and executing plugins for individual objects such as pedestrians, cars, roads, and buildings, a system requirement service for listening to desired functional /non-functional requirements and assisting in the preparation of systemization requirements definition documents, as well as an installation service for constructing user environments in accordance with systemization requirements definition documents.