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DATAmatic Edge Data Management

Automotive Data Challenges

Connected vehicles have quickly become the norm, and the data generated by those vehicles is exponentially increasing in volume, diversity, and economic value to automakers and their ecosystems. Additionally, the continued evolution of electric drivetrain, autonomous driving, personalized services, and real-time applications is reliant on access to data and analytics both on-board the vehicle at the “edge” as well as off-board in the cloud. The ability to maximize data value extraction while mitigating transmission expenses, working within target device resource limitations, and protecting consumer privacy will be essential to the success of any automaker’s data strategy and related programs.

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Airbiquity addresses these challenges with the DATAmatic Edge Data Platform, a comprehensive solution and tool set for dynamic data acquisition and edge analytics.

  • Leverages a hybrid edge and cloud environment to give automakers the control they need across the entire data management lifecycle
  • Provides a secure and sophisticated analytic module development environment and deployment capability
  • Supports a variety of traditional and distributed electrical architectures easing integration into automotive vehicle platforms, systems, and components
  • Provides a resilient run time environment, enabling analytic module portability while ensuring proper use of target device resources

 

DATAmatic Edge Data Platform Features

 

Data Asset Identifier

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Easily identify what data elements are available from individual vehicles, ECUs, and analytic modules

 

Data Acquisition Controller

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Dynamically designate what data parameters are collected at what frequency and for what duration

 

Data Package Generator

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Create resource efficient edge data analytic modules that are optimized to run on many target devices

 

Data Package Validator

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Simplify analytic module development testing and validation using readily available tool chains and emulators

 

Data Shield

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Address data security and privacy while employing state of the art data compression

 

Data Monitor

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Ensure data analytics activity does not compromise system function of target devices, systems, and vehicles

 

Efficient Data Collection

A fundamental step in connected vehicle data management is the selection and collection of data sets to power analytics in the cloud. One approach is to collect­­—or log—all the data generated by the vehicle, but this approach is increasingly inefficient due to the cost of transmitting large amounts of data from the vehicle and storing it in the cloud. Another more efficient approach is to dynamically log data sets that align with specific analytic objectives, some of which may change over time.

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Airbiquity enables efficient data logging with the DATAmatic Edge Data Logger, a simple to integrate and use product that allows secure dynamic data logging for both on-board edge and cloud-based analytics.

  • Dynamically configure what data is logged, with what frequency, and under what conditions
  • Maximizes data set availability for analytics while mitigating data transmission expense
  • Optimized to work with resource limited ECUs thereby easing integration into automotive vehicle platforms, systems, and components

DATAmatic Edge Data Logger Features

 

Data Asset Identifier

Identifier@2x.png
Easily identify what data elements are available from individual vehicles, ECUs, and analytic modules

 

Data Acquisition Controller

Controller@2x.png
Dynamically designate what data parameters are collected at what frequency and for what duration

 

Data Shield

Sheild@2x.png
Address data security and privacy while employing state of the art data compression

 

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DATAmatic Edge Analytic Bots are a preprogrammed library of edge analytic modules that allow DATAmatic customers to execute edge data analytics more quickly and easily.

  • Common data reduction and optimization use cases such as histograms, performance threshold alerts, and rate of change
  • Industry specific value added applications

 

Technology Integrations

 

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