Entity-Relationship Index for Cross-Source Data Integration
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Solution Overview
Problem
Current systems fail to effectively associate and index data records from multiple information sources that contain information about the same entity, due to issues like data fragmentation, typographical errors, and differing data syntax, leading to incomplete or incorrect data retrieval in databases.
Innovation Solution
A system and method that links and integrates data records from various information sources into existing hierarchies, using attribute matching and confidence scoring to associate records and reconcile hierarchies, allowing for accurate identification and retrieval of relevant data records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data records from multiple information sources are stored separately in databases, then data can be maintained in its original format and source integrity is preserved, but data fragmentation occurs and relevant information about the same entity cannot be retrieved together
Solution Approach 1:
The system segments data management into two distinct layers: the original data records remain intact in their source databases, while a separate entity-relationship index structure is created that maps entities to their corresponding data records across multiple sources. This segmentation allows simultaneous preservation of source integrity and enablement of cross-source entity retrieval.
Solution Approach 2:
The patent introduces an intermediary entity-relationship index that acts as a mediator between the original data sources and query operations. This index contains entity identifiers and relationships that enable the system to locate and retrieve all data records pertaining to a specific entity across multiple information sources without modifying the original data storage structure.
2Adaptability or versatility
If data entry is performed manually to populate databases, then data can be customized and formatted, but typographical errors and data entry mistakes occur that prevent accurate entity identification
Solution Approach 1:
The system implements self-service through automated entity identification and matching mechanisms. The entity-relationship index automatically processes and standardizes entity identifiers from multiple sources, using algorithms to match variants of the same entity (e.g., different spellings or formats) without requiring manual intervention, thereby reducing typographical errors while maintaining formatting flexibility.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously refines entity matching based on identified relationships and patterns. When entities are matched across sources, the system learns from these matches to improve future identification accuracy, creating a self-correcting system that reduces the impact of initial data entry errors.
3Quantity of substance
If multiple separate data records are created for the same entity from different information sources, then all available information about the entity is captured, but the records cannot be associated and retrieval becomes inefficient
Solution Approach 1:
The patent merges multiple separate data records into a unified entity view through the entity-relationship index. By creating entity identifiers that group all data records pertaining to the same entity across different information sources, the system combines the completeness of multiple sources with the efficiency of unified retrieval, allowing users to access all entity information through a single query.
4Adaptability or versatility
If data records with different syntax and formats from multiple information sources are integrated, then comprehensive data coverage is achieved, but the complexity of matching and associating records increases
Solution Approach 1:
The system applies local quality by handling each information source according to its specific characteristics and data format requirements in the entity-relationship index construction process, while maintaining a unified entity identification framework. This allows the system to accommodate diverse data sources with different syntax and formats without requiring complex global transformation rules.
Data Source
AI summary
Systems and methods for indexing, associating or compositing data records and hierarchies from various information sources are disclosed. Embodiments of the present invention may provide the ability to link data records and thus to link data records to known hierarchies of data records. More specifically, embodiments of the present invention may provide the capability to associate data records in varying information sources and to thereby associate incoming data record with existing data records or existing data hierarchies such that an incoming data record may not only be associated with an existing data record comprising information about the same entity but may additionally be associated with other members of the data hierarchy in the same manner as the existing data record. In addition to associating an incoming data record with an existing data record and incorporating the incoming data record into an existing data hierarchy, embodiments of the present invention may provide the capability of reconciling an incoming data hierarchy to which an incoming data record belongs with an existing data hierarchy belongs such that the two data hierarchies may be composited.