Distributed BRIX

The automotive industry is in a wave of transformation with innovations like emission regulations for Gasoline & diesel engine Electric/Hybrid mobility, Automated Driver Assistance Systems (ADAS), Autonomous Driving (AD). The IT teams of organizations in this domain are wagering on big data technologies (cloud & cluster computing). To assure competitiveness, firms in the automotive industry are forced to respond to market demands quickly and to develop and manufacture their products efficiently. One such initiative called Digital twin (where simulation and physical test result meet)strategy is being realized in the product validation area to fulfil high-quality standards, meet customer needs & legal requirements to expedite time to market. However, there remain challenges to overcome to realize the strategy.

Simulation data is already in digital form. Bringing physical test & validation data in digital form is challenging, given the scattered existence of data in different function groups, departments, formats and increased volume. The lack of engineering domain knowhow is an equal drawback asIT challenge. To address this is Distributed Brix – Big Data – a scalable platform based on distributed and parallel computing architecture coupled with the knowledge of complex engineering data in automotive domain for the past two decades.

Given the nature of high-volume engineering data in Automotive, concurrent computing resources are required to process it, which can effectively process algorithms in parallel. Distributed Brix offers high-performance parallel computing architecture coupled with well-defined semantics meta model for domain know-how. Once Digital twin is realized in product validation area, OEMs will have the foundation for AI and ML. In fact, specifically in the R&D environment, data sources like test beds & development fleet can become IoT device. Data from different validation testing equipment or development vehicle fleet (IoT Devices) will flow into the development area.