Entity Resolution Rule Engine for Duplicate Enterprise Records
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Solution Overview
Problem
Existing systems face challenges in accurately and efficiently managing and resolving enterprise entities due to duplicate or incorrect data, which is time-consuming and prone to errors, especially when dealing with a large number of entities, and there is a need for automated tools that can manage and update entity information effectively.
Innovation Solution
A back-end application computer server system that includes an entity data store and a resolution rule library, utilizing an ingestion engine to automatically resolve entities with different identifiers as a single entity, update the data store, and execute enterprise workflows based on resolution rules, supported by an interactive graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entity resolution is used, then accuracy can be maintained, but time consumption increases significantly
Solution Approach 1:
The system performs automated entity resolution by applying resolution rules to match electronic records with different entity identifiers to the same entity, eliminating the need for manual intervention while maintaining accuracy through rule-based processing
Solution Approach 2:
The patent replaces manual mechanical entity resolution processes with an automated computer-based system that uses resolution rules and algorithms to resolve entities, substituting human labor with computational mechanisms
2Productivity
If automated entity resolution is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The system segments the entity resolution process into distinct components: data ingestion, rule application, entity matching, and workflow execution, making the complex automated system manageable through modular functional separation
Solution Approach 2:
The patent introduces resolution rules as intermediary elements that mediate between incoming electronic records and the entity data store, simplifying the automated matching process through predefined criteria rather than complex real-time decision logic
3Quantity of substance
If duplicate entity records are present, then data completeness is maintained, but data quality deteriorates
Solution Approach 1:
The system merges duplicate electronic records by applying resolution rules to identify records representing the same entity, consolidating them into a single unified record while preserving comprehensive information through the merging process
Solution Approach 2:
The automated resolution system provides feedback by continuously monitoring entity data and adjusting resolutions based on the application of resolution rules, ensuring data quality improvement while maintaining completeness through iterative processing
Data Source
AI summary
An entity data store contains electronic records associated with entities (e.g., businesses). For each entity, electronic records include an entity identifier, entity operational data, and parameters associated with that entity (e.g., business name, address, etc.). A resolution rule library contains electronic records associated with resolution rules, including a rule identifier and rule logic. An ingestion engine of a computer server receives big data input and accesses the resolution rule library. Based on resolution rule logic and the received big data input, the computer server automatically resolves that two electronic records with different entity identifiers are associated with a single entity. The computer server can then update the entity data store to reflect information for a resolved single entity identifier and automatically execute an enterprise workflow for a risk relationship between the enterprise and the entity represented by the resolved single entity identifier.