Automated Schema Mapping Evolution via Local Scope Analysis
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Solution Overview
Problem
Manual updates of mappings between conceptual models and physical database schemas are cumbersome and difficult, especially when changes occur, as they require manual modification of the schema and mapping, which can be complex and time-consuming.
Innovation Solution
Automating the evolution of schemas and mappings by determining the local scope of changes, identifying patterns in existing mappings, and updating mappings accordingly to preserve existing data, using techniques such as Table-per-Type, Table-per-Concrete Class, and Table-per-Hierarchy schemes, and translating these updates into changes to the physical database schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates of mappings between conceptual models and physical database schemas are performed, then data integrity can be maintained through careful review, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary analysis of the conceptual model change to automatically determine the local scope and identify affected mappings before manual intervention is needed. This preliminary action prepares the groundwork for efficient updating while maintaining data integrity through pre-validation of the change impact.
Solution Approach 2:
The mapping update process serves itself by automatically detecting changes in the conceptual model, determining the local scope of affected mappings, and applying updates based on identified patterns. The system autonomously handles the tedious aspects of mapping evolution while preserving data integrity through built-in validation mechanisms.
2Adaptability or versatility
If manual modification of schema and mapping is performed to reflect application changes, then adaptability to new requirements is achieved, but the complexity and difficulty of the process increases
Solution Approach 1:
The system segments the complex mapping update process into distinct automated stages: change detection in the conceptual model, local scope determination, pattern identification from existing mappings, and selective application of updates. This segmentation reduces perceived complexity by automating each segment while maintaining adaptability to various types of conceptual model changes.
Solution Approach 2:
The system introduces an intermediary automated evolution process between the conceptual model and the physical database schema. This intermediary automatically analyzes changes, determines their local scope, identifies appropriate mapping patterns, and applies transformations, thereby reducing the complexity burden on developers while maintaining full adaptability to application changes.
3Productivity
If automated updates are applied to mappings based on conceptual model changes, then productivity is improved, but the risk of errors in pattern recognition may increase
Solution Approach 1:
The system applies local quality by determining a local scope around each change in the conceptual model and identifying mapping patterns specific to that local context rather than applying global patterns universally. This localized approach improves productivity by being more precise about which mappings to update while reducing error risk by considering the specific characteristics of each local context.
Solution Approach 2:
The automated evolution system incorporates feedback mechanisms where the identified local scope and patterns are validated against existing mappings before application. The system learns from the structure and patterns in existing mappings to improve its pattern recognition accuracy, creating a feedback loop that enhances both productivity and reliability over time.
Data Source
AI summary
Aspects of the subject matter described herein relate to automating evolution of schemas and mappings. In aspects, mappings between a conceptual model and a store model are updated automatically in response to a change that occurs to the conceptual model. For example, when a change occurs to the conceptual model, a local scope of the change is determined. The local scope indicates mappings that are most similar to the type(s) affected by the change. Based on the local scope, a pattern of mappings between the conceptual model and the store model is determined. Using this pattern and the nature of the change, the mappings are updated according to the pattern. In addition, the store model and data thereon may be updated in a manner to preserve existing data that is not to be deleted in response to the change.