Cross-Stream Data Integration for Real-Time Distributed File Updates
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Solution Overview
Problem
Traditional computing architectures are not well-suited for updating distributed data files in real-time, especially in dynamic environments, and fail to optimize engagement with users across various communication channels, lacking scalability and failing to consider user classifications associated with entities delivering goods or services.
Innovation Solution
A cross-stream data processor that automatically updates or modifies compatible distributed data files by correlating event data across multiple data streams, using machine learning algorithms to identify and integrate data in real-time, leveraging asynchronous messaging services for scalable data throughput and compatibility analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional server architectures are used, then code deployment and maintenance is straightforward, but scalability and real-time data updating capability are hindered
Solution Approach 1:
The patent replaces traditional mechanical server-based code deployment with an automated machine learning system that generates and deploys code updates. The ML model analyzes event data, determines code modifications, and automatically updates distributed data files without requiring manual server intervention, thereby enabling real-time updates while reducing operational complexity
Solution Approach 2:
The system implements self-service automation where the machine learning model autonomously monitors event data, identifies necessary code updates, and deploys modifications to distributed data files. This eliminates the need for manual code deployment and server maintenance, allowing the system to scale and update itself in real-time without human intervention
2Speed
If single threaded server architecture is used, then system simplicity is maintained, but API processing speed and scalability are reduced
Solution Approach 1:
The patent segments the server architecture into multiple independent threads that can process API calls concurrently. Each thread handles specific data streams or event sources independently, allowing parallel processing of API requests. This segmentation enables the system to maintain simplicity at the high level while implementing complex threading at the operational level to improve processing speed
3Adaptability or versatility
If traditional data updating techniques are used, then data consistency is maintained, but adaptability to dynamic environments and user classifications is insufficient
Solution Approach 1:
The patent implements a feedback loop where the machine learning model continuously monitors event data from distributed sources, analyzes changes in real-time, and automatically generates code updates. This feedback mechanism ensures the system adapts to dynamic environments while maintaining data consistency through controlled, analyzed modifications rather than arbitrary updates
Solution Approach 2:
The system dynamically adjusts its behavior by using machine learning to adapt to changing event patterns and user classifications. The ML model processes real-time event data and modifies data file structures accordingly, allowing the system to respond to dynamic environmental changes while maintaining consistency through intelligent, rather than static, data management
4Productivity
If automated machine learning based system is implemented, then real-time data updating and scalability are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent applies partial action by processing only the most relevant event data through machine learning algorithms, rather than analyzing all incoming data uniformly. The system identifies and prioritizes critical events that require code updates, applying computational resources selectively to high-impact data streams, thereby reducing overall energy consumption while maintaining high data throughput capability
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. Further, a computing platform is configured to receive inputs as natural language to facilitate automatic generation and integration to form a modified distributed file responsive to events, or moments, among other things including data relevant to an entity, which may provide a good or service.