Componentized Data Storage Segmentation
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Solution Overview
Problem
Conventional data storage methods are inefficient for large-scale content searching, post-processing, and indexing due to the challenge of retrieving different data types, such as text, images, and metadata, stored together in the same data file.
Innovation Solution
The system parses data files into separate data components corresponding to different data types, generating database entries that are stored in segregated databases, allowing for efficient searching, access, and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If different data types are stored together in the same data file, then storage simplicity is maintained, but retrieval efficiency and search effectiveness deteriorate
Solution Approach 1:
The patent segments data files by separating different data types (text, images, tables, metadata) into distinct data components. Each data component is stored in a separate database table organized by its data type, enabling targeted retrieval operations. This segmentation resolves the contradiction by sacrificing storage structure simplicity to gain significant retrieval efficiency and search effectiveness.
2Productivity
If data is stored in segregated databases by data type, then search and retrieval efficiency is improved, but storage and system complexity increases
Solution Approach 1:
The patent implements a universal data componentization framework that handles multiple data types through a common architecture. The system uses standardized database schemas and retrieval mechanisms that work across all data types, reducing the complexity burden of segregation. This multi-functional approach allows the system to manage diverse data types efficiently while maintaining relatively manageable system complexity.
3Device complexity
If conventional data storage is used, then system simplicity is maintained, but large-scale content searching and indexing become ineffective
Solution Approach 1:
The patent applies segmentation by dividing monolithic data files into typed data components stored in separate database tables. This enables search algorithms to target specific data types efficiently, dramatically improving large-scale content searching and indexing effectiveness. The segmentation transforms the system from simple but ineffective conventional storage to a more complex but reliable structured storage system.
Data Source
AI summary
There are provided computer systems and methods for performing componentized data storage. Such a system includes a hardware processor, a system memory, and a data componentization unit including a data resolution module and a data archiving module stored in the system memory. The hardware processor is configured to execute the data componentization unit to receive a data file including multiple data types, and to transform the data file into data components corresponding respectively to the multiple data types. The hardware processor is further configured to execute the data componentization unit to generate database entries each including one of the data components, and to store each of the database entries in one of multiple databases based on the data type corresponding to the data component included in the database entry.