Cross-Stream Data Processor for Distributed File Updates
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Solution Overview
Problem
Traditional computing systems and server architectures are not well-suited for updating distributed data files in real-time, as they often require sequential processing of API calls and are limited by single-threaded designs, hindering scalability and efficiency in dynamic computing environments.
Innovation Solution
A cross-stream data processor is implemented to automatically update or modify distributed data files by correlating event data across multiple data streams, using a multi-stream event correlator, diffusivity index controller, and data compatibility controller to identify and integrate compatible data files, enabling real-time or near real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional single-threaded server architectures are used to process API calls, then system stability is maintained, but processing speed and scalability deteriorate due to sequential processing
Solution Approach 1:
The patent segments the monolithic server architecture into multiple independent microservices that can process API calls in parallel. Each microservice handles specific data stream processing tasks, enabling concurrent execution and improving overall processing speed while maintaining system stability through modular design.
Solution Approach 2:
The patent introduces a new architectural dimension by implementing a distributed event correlator that operates across multiple data streams simultaneously. This multi-dimensional processing approach allows the system to handle complex event correlations across different data sources in parallel, rather than sequentially processing single data streams.
2Productivity
If traditional sequential processing is used for updating distributed data files, then data consistency is maintained, but real-time update capability deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-processing and validating data events as they arrive from multiple streams, preparing them for correlation and integration before actual data file updates occur. This pre-processing stage ensures data consistency requirements are met beforehand, enabling faster real-time updates without compromising reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the distributed event correlator continuously monitors update outcomes and adjusts processing parameters in real-time. This feedback loop ensures data consistency is maintained while optimizing real-time update performance, allowing the system to adapt to changing data patterns and maintain reliability under varying loads.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and 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. In some examples, a method may include activating APIs to receive via a message throughput data pipe different data streams, extracting features from data using the APIs, identifying event-related data across data sources, correlating the event-related data to form data representing an even, classifying event-related data into a state classification, determining compatible data at data sources, identifying compatible data, and transmitting integration data to integrate with a data source.