Distributed Data Processing With Simultaneous Component Ingestion
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Solution Overview
Problem
Conventional data processing systems face inefficiencies due to sequential processing, increased complexity, resource redundancy, and time-consuming reprogramming when handling multiple formats and components, leading to slowed processing and potential data bottlenecks.
Innovation Solution
A data processing platform utilizing a plurality of interconnected processing components that can ingest and process data in parallel, allowing for simultaneous data handling across multiple components, with each component generating identifiable outputs, and integrating flexible formats to reduce redundancy and automate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential processing is used, then system complexity is reduced, but processing speed and productivity deteriorate
Solution Approach 1:
The system divides data processing into multiple independent processing components that can operate simultaneously. Each component processes specific portions of data or performs specific processing tasks in parallel, eliminating the sequential bottleneck while managing complexity through modular architecture.
Solution Approach 2:
The patent transitions from sequential time-based processing to parallel spatial processing by distributing processing components across multiple entities. This dimensional shift from single-threaded to multi-threaded execution enables simultaneous processing operations, significantly improving productivity.
2Productivity
If multiple processing components are added, then processing capability improves, but resource redundancy increases
Solution Approach 1:
Processing components are designed with universal interfaces and standardized protocols that enable them to handle multiple data formats and perform various processing functions. This multi-functionality reduces the need for dedicated resources for each function, thereby reducing resource redundancy while maintaining high processing capability.
Solution Approach 2:
Multiple processing functions are merged into unified processing components that can operate together in parallel. By combining similar functions and consolidating resources, the system achieves high processing capability without proportionally increasing resource redundancy.
3Manufacturing precision
If processing components are specialized for specific formats, then processing precision improves, but adaptability deteriorates
Solution Approach 1:
The system introduces standardized interfaces and protocols as intermediaries between data formats and processing components. These intermediaries translate between different data formats and processing requirements, allowing specialized processing components to maintain precision while the system as a whole remains adaptable to various formats through the intermediary layer.
Data Source
AI summary
One embodiment provides a method for distributed and parallel processing of data within a data processing platform. The platform receives the data to be processed by the data processing platform. The data processing platform includes a plurality of processing components. The plurality of processing components ingest the data. At least two of the plurality of processing components ingest the data simultaneously. At each of a subset of the plurality of processing components an output is generated from the data by processing the data. Each output includes an identifier indicating the output corresponds to the data. A downstream component receives the output from each of the at least a subset of the plurality of processing components.