Entity Identifier Formation Using Multiple Data Elements
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Solution Overview
Problem
Existing data sources face challenges in identifying and associating entities due to the vast array of records, inaccuracies, and variations in data elements, making it difficult to confidently match records across multiple data sources.
Innovation Solution
The formation of an entity identifier by combining multiple data elements from various data sources, which can be statistically unique to facilitate confident identification and association of entities, with the ability to tailor the uniqueness based on application requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data sources are employed to improve entity identification accuracy, then the reliability of entity identification is improved, but the device complexity and difficulty of detecting and measuring increases due to variations in data element types, amounts, and structures
Solution Approach 1:
The patent creates a universal entity identifier format that can be applied across multiple different data sources with varying data element structures. This universal identifier serves as a common key that works across diverse data sources, eliminating the need for separate identification systems for each data source and reducing overall system complexity.
Solution Approach 2:
The system transforms heterogeneous data elements from various data sources into a standardized parameter format (entity identifier). By changing the parameter representation from source-specific formats to a universal format, the system enables consistent entity identification across different data sources with varying structures.
2Reliability
If multiple data sources are employed to improve entity identification accuracy, then the reliability of entity identification is improved, but the difficulty of detecting and measuring increases due to variations in data element types, amounts, and structures
Solution Approach 1:
The entity identifier acts as an intermediary key that mediates between heterogeneous data sources and the entity matching process. Instead of directly comparing complex, varying data elements from multiple sources, the system uses this intermediate identifier to facilitate straightforward detection and measurement of entity associations.
3Measurement precision
If data elements with evenly distributed values are used to form entity identifiers, then the measurement precision of entity identification is improved, but the loss of information increases by excluding data elements with non-evenly distributed values
Solution Approach 1:
The patent applies different selection criteria to different data elements based on their local characteristics. Data elements with evenly distributed values are selected for entity identifier formation because they provide better precision, while data elements with non-evenly distributed values are excluded or used differently. This local quality approach optimizes identification precision for each specific data element based on its distribution properties.
Data Source
AI summary
Data values from a plurality of data elements can be combined to form one or more entity identifiers to facilitate identifications of and/or associations among a plurality of data records representing one or more entities. Associated data records can represent the same entity and/or multiple entities that can be properly associated. Associations can be made among two or more unique entities and/or their respective representative data records if they correspond to substantially the same entity identifier. In one embodiment, the number, type, and/or characteristics of values for data elements used to form an entity identifier can be selected so that the entity identifier is substantially statistically unique.