Vehicle Data Stream Plug-In Orchestration for Synthetic Attributes
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Solution Overview
Problem
Existing vehicle data streaming systems face challenges in managing and coordinating multiple data stream providers, processing plug-ins, and ensuring relevant data delivery to destinations, leading to inefficiencies and unnecessary network load.
Innovation Solution
A vehicle data streaming service that manages plug-ins to generate synthetic attributes, facilitates data stream associations, and provides a curated catalog for subscribers, while enforcing data schema and access requirements, with options for fully automated or customer-configurable orchestration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system manages multiple data stream providers and plug-ins, then the functionality and versatility of the system is improved, but the device complexity increases
Solution Approach 1:
The system segments functionality into separate plug-ins that can be independently developed, registered, and managed. Each plug-in handles specific data processing tasks, allowing the overall system to manage multiple providers and destinations without requiring a monolithic complex architecture. The plug-in service acts as a mediator that coordinates between data stream providers and destinations.
Solution Approach 2:
The plug-in service functions as an intermediary layer between data stream providers and destinations. It receives data from providers, processes it through registered plug-ins, and delivers to appropriate destinations. This intermediary layer abstracts the complexity of coordinating multiple providers and destinations, simplifying the overall system architecture while maintaining high versatility.
2Loss of information
If the system delivers all vehicle data to destinations, then data completeness is improved, but unnecessary data transfer increases network load
Solution Approach 1:
The system extracts and processes only the relevant data needed by destinations through plug-ins. Instead of transmitting all vehicle data, the plug-ins filter, transform, and select specific data elements based on destination requirements and data schemas. This extraction approach maintains data completeness for subscribed attributes while significantly reducing unnecessary data transfer.
Solution Approach 2:
Different destinations receive customized data streams tailored to their specific needs. The plug-in service applies local quality by filtering and transforming data differently for each destination based on their subscription requirements. This ensures each destination receives only the relevant data it needs, optimizing network load while maintaining completeness for subscribed attributes.
3Ease of operation
If the system provides manual configuration for plug-ins, then ease of operation is improved, but the time required for setup increases
Solution Approach 1:
The system performs preliminary action by automatically configuring plug-ins upon registration. The plug-in service automatically discovers plug-in parameters, validates them against defined schemas, and sets up the necessary data stream connections before the plug-in is fully deployed. This automated preliminary configuration eliminates manual setup time while maintaining operational ease through self-service registration.
Solution Approach 2:
The plug-in registration and configuration process is designed as a self-service mechanism. Plug-ins automatically provide their own configuration information, and the plug-in service autonomously validates and applies the necessary settings. This self-service approach eliminates the need for manual configuration while maintaining ease of operation through intuitive registration processes.
4Reliability
If the system enforces strict data schema validation, then data quality and reliability are improved, but processing overhead increases
Solution Approach 1:
The system performs preliminary validation by checking data schemas before data processing begins. The plug-in service validates data formats and structures in advance, ensuring compliance with defined schemas before data is transmitted or processed further. This preliminary validation approach ensures data quality and reliability while minimizing processing overhead by preventing invalid data from entering the processing pipeline.
Data Source
AI summary
A vehicle data streaming service may receive requests to register plug-ins to generate synthetic vehicle attribute data streams. A plug-in service of the vehicle data streaming service may configure a given plug-in in an internal containerized environment in a fully automated manner and/or configure the plug-in in an external compute service environment. The vehicle data streaming service may configure the plug-ins to receive input streams to generate synthetic attribute output streams. The vehicle data streaming service and the plug-in service may allow the synthetic attribute output streams to be associated with synthetic vehicle attributes included, or to be included, in a curated catalog of vehicle attributes. The vehicle data streaming service furthermore allows one or more vehicle data stream destinations to subscribe to the synthetic vehicle attributes included in the catalog.