Database Transition via Pattern Parsing and Segmentation
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Solution Overview
Problem
The transition from legacy relational databases to cloud-based databases is inefficient due to the copying of database structures and content, which does not fully utilize cloud-based storage and results in prolonged processes with exponential growth of joining operations, leading to inefficiencies and errors.
Innovation Solution
A method that parses database instructions to identify patterns, suggests replacements, and translates between SQL and JSON formats, reducing Cartesian products and preventing inoperable instructions by determining table relationships and applying known patterns to optimize database creation and maintenance processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy database structures and content are copied to cloud-based databases, then database transition is achieved, but the process becomes protracted and inefficient due to exponential growth of joining operations
Solution Approach 1:
The patent segments the database transition process by identifying and separating meaningful data partitions from legacy databases. Instead of copying entire databases, the system divides data into logical segments based on business domains and relationships, allowing parallel processing and reducing overall transition time while maintaining data integrity.
Solution Approach 2:
The system performs preliminary analysis of legacy database structures and relationships before the actual transition. By pre-identifying meaningful partitions, relationships, and dependencies, the system prepares optimization strategies in advance, avoiding exponential joining operations during the actual data migration process.
2Stability of the object's composition
If conventional partitioning is maintained in cloud-based database copies, then database structure is preserved, but cloud-based storage advantages are not fully utilized
Solution Approach 1:
The patent implements dynamic partitioning strategies that adapt to cloud-based storage characteristics. The system reorganizes data partitions based on access patterns, relationship types, and cloud storage optimizations, allowing the database structure to evolve from static legacy partitions to dynamic cloud-optimized partitions that maximize storage efficiency and query performance.
Solution Approach 2:
The system changes key structural parameters when transitioning to cloud-based databases, including partitioning schemes, data distribution strategies, and relationship modeling. By modifying these parameters to suit cloud environments, the system achieves both structural integrity and optimized cloud storage utilization.
3Reliability
If massive databases are joined with similarly massive databases, then complete data integration is achieved, but the joining operations grow exponentially and reduce efficiency
Solution Approach 1:
The patent applies segmentation by identifying and separating independent data subsets that can be joined more efficiently. By breaking down massive database joins into smaller, meaningful partitions based on relationships and access patterns, the system reduces the computational complexity from exponential to manageable levels while maintaining complete data integration.
Solution Approach 2:
The system performs preliminary analysis to identify optimal join strategies, relationships, and data subsets before execution. By pre-planning the join operations and identifying meaningful partitions, the system avoids exponential growth and achieves efficient data integration through optimized join sequences and filtering.
Data Source
AI summary
A system, method, and computer-readable medium for proving feedback on database instructions, identifying, for example, existing patterns and providing suggested replacement instructions. This may have the effect of improving the efficiency of instructions used to create and/or manipulate databases. According to some aspects, these and other benefits may be achieved by parsing received instructions into an organizational structure, traversing the organizational structure for known patterns, and suggesting replacement patterns. In implementation, this may be effected by receiving one or more sets of known patterns and corresponding replacement patterns, parsing received instructions, comparing the known patterns with the parsed instructions, and providing suggested replacement patterns based on one or more known patterns matching the parsed instructions. A benefit of may include reducing Cartesian products during the merging of tables.