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ABOUT NORCOM

Infrastructure for driver assistance systems

The task

Driver assistance systems electronically support the driver of a car in certain driving situations. The aim is to increase the security and reliability of the systems.

 

The challenge

The development of such systems requires a novel architecture for storing, processing and analyzing large amounts of data. Data must be made available for processing quickly - even if it is generated worldwide or stored locally. Likewise, large amounts of data (petabyte range) must be able to be processed quickly - by different teams, regardless of location.

 

our solution
The central solution component is the creation of automated, robust and scalable workflows for the conversion and provision of data and for the automated evaluation of abnormalities as well as the connection to advanced analytics and machine learning for further processing in self-service. The basis is provided by the CI / CD routes developed in the project, with which software products are automatically and robustly rolled out in a standardized manner across several locations around the world. Existing analysis tools were integrated via middleware layers and thus raised to a big data-capable level.

 

The customer benefit

Large series of measurements generated worldwide are now immediately accessible for processing. Anomalies in processes and data arerecognized early and operational failures are avoided. Flexible and customizable analysis solutions ensure the required high speed of innovation.

Project-

Characteristics

Our role

Support of the customer by architects, data scientists, data engineers and software developers

Our activities

  • Parallelization of existing analysis tools

  • Persistence of the analysis results

  • Export of relevant measurement signals and provision for existing visualization tool

Technologies & methods

  • Applications: Jupyter, Pycharm, Zeppelin, Jenkins, Bitbucket, Artifactory, Jira, Confluence, Sonar, DaSense, Grafana

  • Data / databases: Hbase, Hive, OpenTSDB, MDF, ADTF, Parquet

  • Languages / frameworks: Python, Java, Airflow, Hadoop / MapR, Spark, Yarn, Oozie, Docker, Tensorflow, Kerberos, R-Studio

  • Methods: time series / image analysis, deep learning, CI / CD

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