Schema Alignment via Structural Penalty Scores
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Solution Overview
Problem
Integrating complex enterprise data models and database schemas is challenging due to their large size and complexity, leading to data silos, duplicative data, and inefficiencies, as existing methods rely heavily on expensive domain experts and are often language-dependent, limiting their applicability.
Innovation Solution
A method for mapping and aligning database models and schemas using structural data mapping, which calculates penalty scores to rank nodes and select corresponding elements based on distance and relationships, enabling automated or semi-automated data mapping without relying on human-readable names or language constructs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching by domain experts is used, then mapping accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The system performs self-service by automatically calculating penalty scores and ranking nodes based on structural similarities, enabling the mapping process to be executed without continuous human intervention while maintaining high accuracy through algorithmic analysis of schema structures
Solution Approach 2:
The patent replaces the mechanical system of manual expert review with an automated computational system that uses penalty score calculations and graph-based structural analysis to identify corresponding nodes between schemas, significantly reducing time consumption while preserving mapping quality
2Measurement precision
If manual matching by domain experts is used, then mapping accuracy is improved, but cost increases
Solution Approach 1:
The system performs self-service by automatically calculating penalty scores and ranking nodes based on structural similarities, enabling the mapping process to be executed without continuous human intervention while maintaining high accuracy through algorithmic analysis of schema structures
Solution Approach 2:
The patent replaces the mechanical system of manual expert review with an automated computational system that uses penalty score calculations and graph-based structural analysis to identify corresponding nodes between schemas, significantly reducing time consumption while preserving mapping quality
3Reliability
If computational or semi-automated matching is used, then cost is reduced, but effectiveness and applicability worsen due to language dependency
Solution Approach 1:
The patent extracts and removes language-dependent elements from the matching process by focusing exclusively on structural properties of schemas such as node connections, relationship types, and path configurations, making the system universally applicable across different languages and domains
Solution Approach 2:
The patent changes the parameters used for matching from language-based attributes to structure-based attributes, calculating penalty scores based on structural similarities rather than textual or linguistic features, thereby achieving language independence while maintaining cost efficiency
4Productivity
If automated mapping is implemented, then productivity is improved, but handling of complex schemas worsens due to lack of expert knowledge
Solution Approach 1:
The patent applies partial action by calculating penalty scores for only the most relevant structural features and relationships rather than analyzing every possible attribute, enabling fast processing of complex schemas while maintaining sufficient precision for accurate mapping
Solution Approach 2:
The system incorporates feedback mechanisms where penalty score calculations guide the ranking and selection of candidate nodes, with the scoring feedback continuously refining the matching process to handle complex schema structures effectively without requiring expert intervention
Data Source
AI summary
A method for aligning data model schemas is provided herein. A first schema and a second schema may be received. The schemas may include sets of nodes and links between the nodes. An anchor point between the first schema and the second schema may be received. A source node in the first schema may be identified to be mapped to the second schema. A source distance may be calculated between the source node and the anchor point in the first schema. Option distances may be calculated between the anchor point and the other nodes in the second schema. Penalty scores may be calculated for the option distances. A mapping node may be selected from the nodes in the second schema based on their penalty scores. A new anchor point identifying a correspondence between the source node and the mapping node may be stored.