Containerized Vehicle Data Processing via Event Streaming
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Solution Overview
Problem
Existing technologies face challenges in managing large-scale collaboration of multiple data streams from vehicles, particularly in processing and analyzing time-sensitive and non-time-sensitive data effectively.
Innovation Solution
The implementation of a data container platform that combines a distributed event streaming system and various applications, utilizing a Lambda Architecture Framework and event stream processing to ingest, validate, and process vehicular data, enabling real-time data-driven decisioning and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple data streams from vehicles are processed and analyzed, then data analysis capability and response generation are improved, but system complexity and difficulty of managing large-scale collaboration increase
Solution Approach 1:
The patent segments the complex data processing system into multiple independent computing applications deployed as containers on separate computing nodes. Each application handles specific data stream processing tasks, allowing the system to manage large-scale collaboration by dividing the overall processing function into manageable, independently deployable units that can be orchestrated through standardized interfaces.
2Loss of time
If real-time processing of vehicular data is implemented, then response time is reduced, but processing complexity and resource requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring computing applications as containers that are ready to process data streams immediately upon deployment. The system pre-establishes the processing pipeline and computational resources, so when vehicular data arrives, the pre-prepared applications can process it in real-time without requiring complex runtime setup or resource allocation decisions.
3Productivity
If distributed computing applications are deployed for data processing, then data processing capacity is improved, but deployment and management difficulty increase
Solution Approach 1:
The patent creates a universal deployment framework where computing applications are standardized as containers that can run on any computing node in the distributed system. This multi-functional approach allows the same container format and deployment mechanism to handle different types of data processing applications, simplifying deployment and management across the distributed infrastructure while maintaining high processing capacity.
Data Source
AI summary
Approaches, techniques, and mechanisms are disclosed for large scale vehicle data collaborative analysis. According to one embodiment, a large amount of data streams is received from a multitude of vehicles. A distributed event streaming system is applied to parse the data streams based on an attribute, such as time sensitive data, location-specific data, or vehicle maintenance, operational, or fault-prevention data. A data container platform instance hosts applications that receive the parsed data streams. Output of an application is transformed into data streams having a common topic. Other applications may access the data streams by topic. Upon receiving an indication that an application has processed a data stream by topic, a decision point may be reached and executed by an external application, triggering an action on a vehicle from the multitude of vehicles.