Cross-Stream Message Correlation for Distributed Data 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 are agnostic to user classifications, hindering scalability and optimization of engagement with customers.
Innovation Solution
A cross-stream data processor that analyzes electronic messages across multiple data streams to identify compatible distributed data files, facilitating real-time updates and integration using machine learning algorithms and natural language processing to determine compatibility and diffusivity of event data.
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 update capability are hindered
Solution Approach 1:
The patent replaces traditional mechanical server-based code deployment with a cloud-based event-driven architecture using webhooks and distributed data files. This substitution enables real-time data updates across multiple servers without requiring complex centralized control, resolving the contradiction between real-time capability and architectural complexity.
Solution Approach 2:
The system segments data storage into distributed data files across multiple servers rather than using a centralized database. Each server maintains independent copies of data, allowing parallel updates without blocking other operations. This segmentation enables high scalability and real-time updates while simplifying individual server architecture.
2Productivity
If single-threaded database servers are used, then implementation is simple, but API processing speed and scalability are reduced
Solution Approach 1:
The patent divides the single-threaded database into multiple independent servers, each handling specific API requests concurrently. By segmenting the processing load across multiple servers rather than using multi-threading within a single server, the system achieves high API throughput without the complexity of thread management and locking.
Solution Approach 2:
Instead of solving scalability through vertical scaling (adding threads to a single server), the patent transitions to horizontal scaling by adding more servers. This dimensional shift from single-server multi-threading to multi-server single-threaded architecture enables linear scalability while maintaining implementation simplicity.
3Adaptability or versatility
If traditional computing architectures are used, then system stability is maintained, but adaptability to dynamic environments and user classifications is limited
Solution Approach 1:
The patent implements a dynamic architecture where data files are automatically updated in real-time based on events and user classifications. The system adapts to changing environments by continuously receiving updates via webhooks and redistributing data across the server fleet, enabling high adaptability without requiring complex reconfiguration of individual servers.
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.