Document Data Structure Analysis for Identifier Mismatch Resolution
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Solution Overview
Problem
Conventional methods struggle to analyze changes in document data when different identifiers are used, requiring manual confirmation and being burdensome, especially when understanding the document content is necessary.
Innovation Solution
An analysis method that compares first and second document data by reading item value files and definition files to generate structure information, detecting unassociated items, and associating them based on common relationships, even when identifiers differ.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple data matching is performed by comparing identifiers, then the matching process is fast and simple, but it fails to correctly associate items when identifiers have been changed
Solution Approach 1:
The patent performs preliminary actions by extracting structural information from document data before matching, and by pre-processing identifiers to detect potential matches. The system prepares candidate match lists and structural特征 in advance, then uses these pre-prepared materials to speed up the actual matching process while maintaining accuracy through multiple verification stages.
Solution Approach 2:
The patent introduces structural information as an intermediary element between identifiers. When direct identifier matching fails or is insufficient, the system uses structural relationships (parent-child, sibling, etc.) as a mediator to verify and confirm the correct association between items, thereby solving the problem of identifier changes without sacrificing matching accuracy.
2Measurement precision
If manual confirmation of correspondences between items is performed, then matching accuracy is high, but it requires enormous amounts of time and places a large burden on operators
Solution Approach 1:
The patent applies partial action by not requiring complete manual verification of all item correspondences. Instead, the system automatically performs matching using identifiers and structural information, and only requires manual confirmation for cases where the automated system cannot determine correspondence with sufficient confidence, thereby significantly reducing overall analysis time while maintaining high accuracy.
Solution Approach 2:
The system performs self-service by automatically detecting potential matches, extracting structural information, and generating candidate associations without human intervention. The automated matching process handles the majority of cases independently, freeing operators from the burden of manual verification for all items and reducing analysis time dramatically.
3Extent of automation
If common item names and file names are used for data association, then it is possible to perform automated data association, but it is impossible to perform association when identifiers have been changed
Solution Approach 1:
The patent handles parameter changes (identifier changes) by introducing additional matching parameters beyond simple identifier comparison. The system uses structural information (relationships between items, document hierarchy, etc.) as alternative parameters when identifiers have changed, allowing automated association to continue functioning even when the primary identifier parameter has been modified.
Solution Approach 2:
The patent makes the matching system universal by implementing multiple matching mechanisms that can function in different scenarios. The system can switch between identifier-based matching, structure-based matching, and hybrid approaches depending on the situation, thereby maintaining automation capability across various conditions including identifier changes, renamings, and reorganizations.
Data Source
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AI summary
A burden of work for analysis is alleviated. A data structure analysis means (12) reads out document data A (11a) and document data B (11b) from a document data storage means (11), and analyzes the reference relationship between the documents to generate the structure information of the documents. Also, the data structure analysis means (12) analyzes the relationship between items to generate the structure information between the items. A change information analysis means (13) detects unassociated files and unassociated items which are present only in one document. An information matching means (14) associates the unassociated files with one another on the basis of the structure information of the documents. Also, the information matching means (14) associates the unassociated items with one another on the basis of the structure information between the items.