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Semantically Rich Distributed Data Science Platforms - Patterns for Future Standardized Virtual Development in autonomous driving

Mr. Puran Parekh 
Managing Director & CEO
iASYS Technology Solutions Pvt Ltd (India) 

It's time to think beyond traditional big data solutions that are designed to handle textual files instead of structured binary data ! It's time to bring the Big Data hype to usable ground in the Measurement and Simulation domain today and extend it towards scenario based testing in ADAS and Autonomous driving (AD) Validation domain ! The well-designed and proven ASAM ODS Semantics and Data Models will play an indispensable and essential role in Distributed Processing and Machine Learning in the Measurement and Simulation domain. New age data analytics platform like (Jupyter(Python) , Distributed Matlab , Tensor Flow etc.) needs a distributed data platform where structured binary data chunks can be addressed seamlessly (as multi dimensional matrix) by Artificial Intelligence and Machine learning algorithms This presentation is intended to give an educational insight into challenges and solutions of distributed applications processing streams, structured binary files and databases for Validation, analysis, reporting, archiving and machine learning. The feasibility of the Distributed computing / parallel computing / Edge computing Approach will be demonstrated by big data analytics tool Like Jupyter(Python) , Matlab with a new breed of product (Distributed Brix) in a combined Microsoft Azure/Linux environment bringing Cloud Computing for Measurement and Simulation at your fingertips at unprecedented speed levels in deployment, execution and maintenance.