Distributed Data Processing System for Diverse User Interfaces
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Solution Overview
Problem
Conventional parallel/distributed systems are limited in handling the generation, analysis, storage, and consumption of diverse data types, such as image, audio, and sensor data, and lack user-centric design for efficient data processing and interface provision.
Innovation Solution
A data processing system that includes data generation, analysis, and storage means, capable of processing data from various sources and providing user interfaces, utilizing predefined metadata for analysis and storage, and a consumption unit for delivering results to user terminals in a parallel and distributed manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional parallel/distributed systems are used for data processing, then data input, storage and processing can be performed, but the system lacks user-centric design and cannot provide diverse user interfaces
Solution Approach 1:
The system is divided into distinct functional modules: data generation means, data analysis means, data storage means, and data consumption means. Each module handles specific tasks independently, allowing the system to process diverse data types (image, audio, sensor data) through specialized components while maintaining overall system coherence.
Solution Approach 2:
The data analysis means is designed to perform multiple functions: analyzing data from various sources, storing analysis results, and providing data in different formats. This multi-functional design enables the system to serve multiple user needs through a single integrated platform, providing diverse user interfaces without requiring separate systems for each function.
2Adaptability or versatility
If diverse data types (image, audio, sensor data) are processed, then comprehensive analysis and user scenarios can be achieved, but data size increases and analysis difficulty rises
Solution Approach 1:
Different data types are processed by specialized analysis components within the data analysis means. Image data, audio data, and sensor data are handled through distinct processing pathways, allowing each data type to be analyzed using appropriate methods without overwhelming a single processing unit.
Solution Approach 2:
The data analysis means acts as an intermediary between raw data collection and user consumption. It standardizes diverse data formats into a unified analysis framework, making complex multi-source data manageable through intermediate processing layers that translate various data types into comparable formats.
3Productivity
If data is collected from multiple routes and analyzed comprehensively, then diverse user scenarios can be provided, but processing time and system complexity increase
Solution Approach 1:
Data from multiple sources is collected and pre-processed simultaneously through parallel data collection pathways. The data generation means and data analysis means operate concurrently to prepare data for analysis, reducing overall processing time by performing preliminary actions before comprehensive analysis is required.
Solution Approach 2:
The system maintains continuous data flow from generation through analysis to consumption. Rather than batch processing, the parallel distributed architecture enables continuous analysis of incoming data streams, ensuring that user interfaces are provided with minimal delay while maintaining comprehensive data analysis.
Data Source
AI summary
Provided is a data processing system providing various user interfaces to users by analyzing and processing data collected via various routes, by using a parallel and distributive manner based on the assumption that generation, analysis, storage, and consumption of data is performed at various locations.