Configurable Entity Matching System with Dynamic Query Optimization
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Solution Overview
Problem
Identifying and matching multiple representations of the same real-world entity across diverse and heterogeneous data sources is challenging due to limited data quality, incomplete data, and inconsistencies, as well as the sheer volume of data which makes manual matching impractical.
Innovation Solution
A configurable entity matching system that dynamically determines the best processes to query multiple data sources for potential candidates, optimizes the matching process, and provides a feedback loop to continuously improve the query and matching workflows, thereby recognizing like entities across different data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching methods are used to identify entities across data sources, then matching accuracy can be maintained, but productivity is severely limited due to the sheer volume of data
Solution Approach 1:
The patent segments the entity matching process into distinct modular components: data extraction from multiple sources, entity normalization to standard formats, feature generation from normalized data, and matching algorithm application. This segmentation enables automated processing of large volumes while maintaining accuracy through specialized handling at each stage.
Solution Approach 2:
The patent replaces manual mechanical matching processes with automated computational systems that use machine learning algorithms, statistical methods, and computer vision techniques to identify and match entities across diverse data sources, dramatically increasing productivity while maintaining or improving accuracy.
2Reliability
If comprehensive data collection from multiple heterogeneous sources is performed, then matching completeness is improved, but device complexity increases due to data quality variations and inconsistencies
Solution Approach 1:
The patent introduces an intermediary normalization layer that standardizes data from multiple heterogeneous sources into a common format. This intermediary processing stage handles data quality variations and inconsistencies by applying统一的 transformation rules, thereby improving matching completeness without proportionally increasing overall system complexity.
Solution Approach 2:
The patent transforms diverse data parameters from different sources into standardized parameters through normalization processes. By changing the representation form of data while preserving essential information, the system can comprehensively collect data from multiple sources while managing complexity through consistent parameter handling.
3Measurement precision
If dynamic optimization with feedback loops is implemented, then matching precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing data during extraction and normalization stages, generating features and creating standardized representations before the actual matching process. This preliminary preparation reduces the computational burden during iterative optimization and feedback processing, allowing precision improvement with controlled resource consumption.
Solution Approach 2:
The patent implements periodic feedback loops where matching results are evaluated at intervals, and optimization adjustments are made based on accumulated performance data. This periodic rather than continuous optimization reduces computational resource consumption while still achieving precision improvements through systematic refinement.
Data Source
AI summary
Systems and methods are provided for receiving an input comprising one or more attributes, selecting a subset of query options from a list of query options relevant to the attributes of the input, and based on query optimization results from an audit of previous queries, determining a priority order to execute each query in the set of queries based on the query optimization results, and executing each query in the priority order to generate a candidate list. For each candidate in the list of candidates, systems and methods are provided for selecting a subset of available workflows based on relevance to the candidate and based on workflow optimization results, determining an order in which the selected subset of workflows is to be executed, and executing the selected subset of workflows in the determined order to generate a match score indicating the probability that the candidate matches the input.