Dynamic Probabilistic Data Matching Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data matching systems, particularly in healthcare, face challenges in accurately matching data records across disparate sources due to reliance on deterministic methods and the need for reliable unique identifiers, which are not always available, leading to incomplete reliability in data duplication and accuracy management.
Innovation Solution
A computer system dynamically configures a probabilistic matching algorithm at runtime based on a matching configuration file, modifying the algorithm's logic to compare data records from various sources, using agreement and disagreement weights for each field to generate match scores, and applying modifications such as system-based, condition-based, frequency-based, field-substitution, agreement-weight-maximum-cap, match-caching, and waterfall-matching techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If deterministic matching methods are used, then matching speed is improved, but matching reliability deteriorates when unique identifiers are not available
Solution Approach 1:
The system dynamically changes matching parameters (weights, thresholds, algorithms) based on data characteristics and context. Instead of using fixed deterministic rules, the matching engine adjusts parameters in real-time to optimize both speed and reliability for different data scenarios.
Solution Approach 2:
The patent implements a dynamic matching system where the matching strategy is not static but adapts during execution. The system transitions from rigid deterministic matching to flexible probabilistic matching with runtime adjustments, allowing it to respond to data quality variations and improve reliability without sacrificing speed.
2Reliability
If probabilistic matching algorithms are used, then matching reliability is improved, but system complexity increases
Solution Approach 1:
The complex probabilistic matching system is divided into modular components: data normalization module, feature extraction module, weight calculation module, and scoring module. Each component handles a specific aspect of the matching process, making the overall complex system manageable and maintainable while achieving high reliability.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters rather than implementing complex hard-coded logic. Configuration files and parameter tables allow flexible control of matching behavior without modifying the underlying algorithm structure, simplifying system maintenance and adaptation.
3Ease of operation
If static field comparison is used, then ease of operation is improved, but adaptability to different data sources deteriorates
Solution Approach 1:
The matching engine is designed as a universal system that can handle multiple data types, formats, and sources through a unified interface. It supports various matching strategies (deterministic, probabilistic, fuzzy) and can be configured for different domains (healthcare, e-commerce, finance) without requiring separate systems, thus maintaining ease of operation while achieving broad adaptability.
Solution Approach 2:
The system achieves adaptability through parameter configuration rather than structural modification. By changing weights, thresholds, and algorithm parameters in configuration files, the same core engine adapts to different data sources and requirements, maintaining operational simplicity while providing versatile functionality.
4Adaptability or versatility
If dynamic configuration at runtime is implemented, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing data characteristics, feature importance scores, and optimal matching parameters in configuration files and caches during off-peak periods. This allows runtime dynamic adaptation without performing heavy computations during actual matching operations, thus maintaining both adaptability and processing efficiency.
Solution Approach 2:
The matching engine implements self-service by automatically analyzing incoming data characteristics and selecting appropriate matching strategies and parameters without external intervention. It uses built-in heuristics and learned patterns to make real-time decisions, reducing the overhead of dynamic configuration while maintaining high adaptability.
Data Source
AI summary
A system is provided that that dynamically matches data originating from one or more data sources. The system analyzes a matching configuration file, where the matching configuration file includes one or more matching configurations. The system modifies a probabilistic matching algorithm of a matching engine at runtime based on the one or more matching configurations and based on two or more data records of the plurality of data records that require matching. The system compares two data records of a plurality of data records using the modified probabilistic matching algorithm. The system generates a match score for the two data records based on the match weight for each data record field.