Automated Trie Match Key Creation for Database Record Matching
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Solution Overview
Problem
Database systems face inefficiencies in matching records due to the need for human-intensive and time-consuming custom index design, especially when dealing with large and complex datasets, and the challenge of handling records with empty or non-standardized field values, which does not scale with increasing dataset complexity.
Innovation Solution
The system creates a trie data structure based on attribute fields with low lookup costs to efficiently identify matching records, using techniques such as tokenization and fuzzy matching to accommodate variations in record values, allowing for automatic index creation and optimization without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If custom indices are designed manually for each dataset, then matching accuracy is improved, but development time and human resources increase significantly
Solution Approach 1:
The system automatically creates and optimizes match keys without human intervention. The automated match key creation process analyzes dataset attributes, determines optimal matching strategies, and generates match keys autonomously, eliminating the need for manual index design while maintaining high matching accuracy
Solution Approach 2:
The system dynamically adjusts match key parameters based on dataset characteristics. By changing parameters such as matching thresholds, attribute weights, and index structures according to the specific dataset being processed, the system achieves high accuracy across diverse datasets without requiring manual reconfiguration
2Reliability
If multiple custom indices are created for complex datasets, then recall is improved, but system complexity increases
Solution Approach 1:
The system employs a universal match key creation framework that handles diverse dataset types and complexities through a single automated process. This multi-functional approach can process simple and complex datasets, adjust to different attribute structures, and optimize match keys accordingly, achieving high recall without proportionally increasing system complexity
Solution Approach 2:
The match key creation process is dynamic and adaptive, automatically adjusting the number and structure of match keys based on the specific dataset being processed. The system analyzes dataset characteristics and dynamically determines the optimal matching strategy, creating only the necessary indices rather than pre-defining multiple fixed indices, thereby maintaining manageable complexity while achieving high recall
3Reliability
If intensive multiple-value matching is applied to all records, then matching completeness is improved, but system resource consumption increases
Solution Approach 1:
The system performs preliminary filtering using automatically created match keys before applying intensive multiple-value matching. By pre-processing datasets to identify and index key matching attributes, the system reduces the candidate record set that requires resource-intensive full matching, thereby achieving complete matching without processing all possible record pairs
Solution Approach 2:
The system applies matching at different levels of intensity based on record characteristics. Rather than applying full intensive matching uniformly to all records, the automated match key creation enables a tiered approach where only records with potential matches based on preliminary key comparison undergo intensive multiple-value matching, reducing overall resource consumption while maintaining completeness
4Ease of manufacture
If traditional matching approaches are used, then implementation simplicity is maintained, but scalability deteriorates with increasing dataset complexity
Solution Approach 1:
The system automatically adapts to increasing dataset complexity without requiring manual reconfiguration. The automated match key creation process analyzes new datasets, determines appropriate matching strategies, and optimizes indices autonomously, enabling the system to scale to handle increasingly complex and larger datasets while maintaining implementation simplicity
Solution Approach 2:
The system dynamically changes matching parameters and index structures based on dataset characteristics and scale. As datasets grow in size and complexity, the automated process adjusts match key creation parameters, attribute selection, and indexing strategies accordingly, maintaining optimal performance and scalability without requiring manual intervention or complex predefined configurations
Data Source
AI summary
A system identifies a first number of distinct values stored in a first field by a dataset of records. The system identifies a second number of distinct values stored in a second field by the dataset of records. The system creates a trie from values stored in a field by multiple records, the field corresponding to the first field or the second field, based on comparing the first number to the second number. The system associates a node in the trie with one of the multiple records, based on a value stored in the field by the record. The system identifies a branch sequence in the trie as a key for a prospective record, based on a prospective value stored in a corresponding field by the prospective record. The system uses the key for the prospective record to identify one of the multiple records that matches the prospective record.