Single-Table Electronic Database for Flexible Data Management
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Solution Overview
Problem
Existing database systems lack flexibility and stability, requiring manual setup of correspondence fields between normalized and denormalized tables, leading to errors and performance degradation when handling large data quantities, and do not allow for efficient data management without additional architectural changes.
Innovation Solution
An electronic database organized as a single table with specific columns and indexes, allowing data to be stored and managed without altering the database structure, providing a universal system for user tasks and hiding technical details, thus eliminating errors and ensuring reliability and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual setup of correspondence fields between normalized and denormalized tables is used, then flexibility in database structure is achieved, but errors and instability occur due to direct user involvement
Solution Approach 1:
The database system automatically manages the correspondence between normalized and denormalized tables through self-service mechanisms. The system autonomously generates and maintains the mapping relationships without requiring manual user setup, thereby eliminating human errors while preserving structural flexibility through automated adaptation.
2Ease of operation
If manual selection of correspondence fields is implemented, then user control over data mapping is improved, but additional charges for duplicate information storing occur
Solution Approach 1:
The system implements feedback mechanisms that automatically track and manage data mappings between normalized and denormalized tables. This feedback loop enables the system to identify and eliminate duplicate information storage while maintaining user-defined mapping preferences, thereby reducing data redundancy without sacrificing operational control.
3Ease of manufacture
If traditional normalized database architecture is used, then data organization is improved, but performance degradation occurs when working with large quantities of data
Solution Approach 1:
The database system dynamically adapts its structure based on data quantity and access patterns. When working with large quantities of data, the system automatically transitions from a purely normalized architecture to a denormalized architecture, optimizing performance while maintaining data organization integrity through dynamic structural transformation.
4Productivity
If additional indexes and data partitions are created to handle large data quantities, then performance is improved, but database architecture complexity increases
Solution Approach 1:
The system creates a universal denormalized table structure that serves multiple functions simultaneously: it handles large data quantities efficiently, provides automatic indexing through its inherent structure, and eliminates the need for separate partitioning mechanisms. This multi-functional approach improves performance without increasing architectural complexity.
Data Source
AI summary
Methods and systems for generating an electronic database, the electronic database comprising database elements being organized in a single table, the single table comprising at least four columns, the at least four columns including: a first column for storing identification numbers of the database elements, a second column for storing a numbers of a parent element for database elements being dependent from other database elements, a third column for storing database elements values, a fourth column for storing a code of the in-use datatypes; and at least five rows, the at least five rows including a first row representing a root element, a second row representing a datatype, a third representing a term, a fourth row representing the term's attribute and a fifth row representing data.