Structured Data Analysis System for Missing Field Detection
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Solution Overview
Problem
Enterprise systems face errors due to missing fields in system data when executing API calls, as they lack the necessary elements to complete tasks, leading to incomplete data processing and potential failures in operations.
Innovation Solution
A structured data analysis system that converts input data into tree structure data, analyzes nodes to identify missing fields, and generates output data by accessing original field data to complete missing fields, ensuring the server can perform operations correctly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the enterprise system uses existing system data to execute API calls, then the system can operate with current data, but the system may lack necessary fields leading to errors and incomplete data processing
Solution Approach 1:
The system performs preliminary analysis of input data structure before API call execution. The processor converts input data to tree structure and identifies missing fields in advance, allowing the system to prepare complete data structures before execution, thus preventing errors and information loss during API calls
2Adaptability or versatility
If the enterprise system updates system data to include new elements, then the system can execute new tasks, but the complexity of data management increases
Solution Approach 1:
The system segments data analysis into distinct hierarchical levels using tree structure. The processor divides input data into collections, nodes, and elements, allowing systematic identification of missing fields at each level. This segmentation simplifies complex data management by organizing it into manageable hierarchical units that can be independently analyzed and updated
3Measurement precision
If the system performs manual checking of missing fields, then data completeness can be verified, but the processing time and operational complexity increase
Solution Approach 1:
The system performs self-service data analysis by automatically converting input data to tree structure and identifying missing fields without manual intervention. The processor autonomously compares actual data structure against required structure, generating completeness results automatically, thus achieving high measurement precision without time loss or operational complexity
Data Source
AI summary
A structured data analysis system and a method for structured data analysis are provided. The structured data analysis system includes a memory and a processor. The processor obtains input data from an electronic device and converts the same into tree structure data to form nodes. The processor selects a first and a second node among the nodes. The first and the second nodes respectively correspond to a first and a second collection among the collections in the input data. The processor generates missing judgment result by judging whether the first node is missing according to a first amount of the first collection and a second amount of elements included in the second collection. The processor judges that whether the processor generates output data to the server based on original field data according to the missing judgment result to enable the server to operate correctly according to the completed output data.