Data Search Apparatus Handling Alias Notations
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Solution Overview
Problem
Existing data search methods fail to accurately identify suitable master data names when faced with notation inconsistencies, particularly aliases and abbreviations, leading to inefficiencies and increased labor in business operations.
Innovation Solution
A data search apparatus that calculates edit distances and similarity degrees using term frequency-inverse document frequency (TF-IDF) and adjacency information to narrow down candidates for data names, enabling accurate identification of corresponding master data names even with alias notations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If notation inconsistencies (aliases) are registered in a dictionary in advance, then search accuracy for known aliases is improved, but all local rules cannot be determined and unregistered aliases cannot be handled
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing edit distances between master data names and potential alias variations. When a search is performed, these pre-computed distance metrics enable rapid comparison and matching without requiring manual dictionary registration of every possible alias, thus handling unregistered aliases effectively while maintaining search accuracy.
2Reliability
If UI augmentation with pull-down menu is adopted, then notation inconsistency is prevented at input location, but it is not effective for DB where abbreviation and alias notation are already present
Solution Approach 1:
The system introduces an intermediary processing layer between data input and database storage. This intermediary automatically detects, analyzes, and standardizes various notation forms (abbreviations, aliases) by comparing them against master data names using edit distance calculations. This allows the system to handle existing notation inconsistencies in the database while still preventing new inconsistencies through automatic standardization.
3Measurement precision
If manual correction of aliases to master data names is performed, then data accuracy is improved, but time and labor are required which hinder business operations
Solution Approach 1:
The system implements self-service by automatically performing the correction function. When data with alias or abbreviated notation is input or queried, the system autonomously compares it against master data names using edit distance metrics, identifies the most likely match, and performs the correction without requiring manual intervention. This maintains high data accuracy while eliminating the time and labor costs associated with manual correction.
4Measurement precision
If edit distance calculation is performed between all master data names and input data names, then accurate matching is achieved, but calculation complexity increases
Solution Approach 1:
The system segments the matching process into multiple stages: first filtering candidates using basic string comparison or prefix matching, then performing detailed edit distance calculations only on the filtered subset. This segmentation reduces the overall calculation complexity while maintaining matching accuracy, as the computationally intensive edit distance calculations are performed on a limited number of candidate matches rather than all master data names.
Data Source
AI summary
A data search apparatus according to an embodiment includes: an input unit; and a storage apparatus configured to store master data names managed with master data. The data search apparatus calculates edit distances between master data names stored in the storage apparatus and input data names input in the input unit, calculates degrees of similarity between the master data names and the input data names based on term frequency and inverse document frequency of the master data names and the input data names, performs processing for narrowing down candidates for the data name being searched for in the master data names based on the calculation results and adjacency information indicating adjacency relationships between the master data names and the input data names, and outputs information indicating correspondence between the master data names and the input data names based on the candidate for the data name being searched for, the candidate for the data name being obtained through the narrowing-down processing.