Metadata Model Technical Keys for Data Persistency
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Solution Overview
Problem
Current data management systems face challenges in efficiently managing and updating metadata models, particularly in handling changes to entity fields, adding transformation rules, and maintaining data consistency across different states of entities.
Innovation Solution
A system that receives a metadata model definition, determines technical keys for entities, and applies changes such as modifying field datatypes, adding transformation rules, and managing validity rules, while storing these changes in records to ensure data persistency and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a metadata model definition is stored with technical keys, then data persistency is improved, but device complexity increases
Solution Approach 1:
The patent introduces technical keys as intermediary elements that mediate between the metadata model definition and the actual data storage. These technical keys serve as a bridge, allowing the system to maintain data persistency by storing mappings between semantic keys (from the metadata model) and technical keys (used for actual data reference). This intermediary layer resolves the contradiction by providing a structured way to manage relationships without excessive complexity.
Solution Approach 2:
The patent segments the data management system into distinct components: metadata model definitions, technical keys, and data records. By separating the metadata (which defines the structure) from the actual data (which uses technical keys for reference), the system achieves better persistency while managing complexity through modular organization. The technical keys are stored separately in mappings, allowing independent management of schema and data.
2Reliability
If transformation rules are applied to existing records, then data consistency is improved, but processing time increases
Solution Approach 1:
The patent applies transformation rules to existing records when metadata model changes are detected. The system proactively identifies records that need to be updated based on the changed metadata definition and applies the appropriate transformations. This preliminary action ensures data consistency is maintained before the changes take effect, resolving the contradiction by ensuring consistency is achieved through targeted processing rather than full reprocessing.
Solution Approach 2:
The patent manages data consistency by tracking parameter changes in the metadata model definition (such as field datatype changes, required field additions, or validation rule modifications). When these parameter changes occur, the system generates appropriate transformation rules and applies them to existing records. This parameter-driven approach ensures consistency is maintained through controlled changes rather than exhaustive processing.
3Adaptability or versatility
If field datatype modifications are made, then adaptability is improved, but manufacturing precision (data integrity) becomes more difficult to maintain
Solution Approach 1:
The patent implements a feedback mechanism that monitors metadata model changes and automatically generates transformation rules to maintain data integrity. When field datatype modifications or other schema changes are made, the system provides feedback by identifying affected records and applying appropriate transformations. This feedback loop ensures that adaptability through schema changes does not compromise data integrity, as the system actively manages the transition.
Solution Approach 2:
The patent prepares for potential data integrity issues by establishing transformation rules in advance of actual data migration. When metadata changes are detected, the system beforehand identifies the scope of impact and prepares transformation logic to handle edge cases. This prior cushioning approach allows flexible schema evolution while protecting data integrity through pre-planned mitigation strategies.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a metadata model definition comprising a set of entity definitions specifying a set of entities, a set of semantic key definitions specifying a set of semantic keys associated with the set of entities, and a set of relationship definitions specifying a set of relationships between the set of entities. The set of semantic keys are configured to be used by an application to refer to the set of entities. The program further determines a set of technical keys for the set of entities. The set of technical keys are configured to be used by the device to refer to the set of entities. The program also stores the metadata model definition and the set of technical keys in a set of records.