Unstructured Data Conversion with Duplicate Text Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Converting unstructured data to structured data is challenging due to the presence of duplicate data in large JSON objects, which complicates the conversion process and lacks a predetermined schema structure.
Innovation Solution
A conversion system that identifies and modifies duplicate text in unstructured data using programmatic solutions, applying mechanisms to change attributes and convert the data into a structured format with reduced duplicates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If unstructured data is converted directly to structured data without preprocessing, then conversion speed is maintained, but data quality and structure integrity deteriorate due to duplicate text
Solution Approach 1:
The system performs preliminary identification and modification of duplicate text in unstructured data before converting to structured format. The duplicate text detection and attribute modification occurs in the input phase, ensuring clean data enters the conversion process, thereby maintaining structure integrity without sacrificing conversion efficiency
2Manufacturing precision
If duplicate text identification and modification is performed during conversion, then data quality improves, but processing complexity increases
Solution Approach 1:
The system merges the duplicate text detection, attribute identification, and data conversion functions into a single integrated processing pipeline. By combining these operations that previously occurred in separate stages, the system reduces overall processing complexity while maintaining data quality improvements
Solution Approach 2:
The processing system is designed with multi-functional capabilities to simultaneously detect duplicate text, identify associated attributes, modify attributes, and convert data formats all within one system. This universal approach avoids the need for multiple specialized systems, thereby reducing complexity while achieving data quality goals
3Loss of substance
If all duplicate text is removed from unstructured data, then file size and storage requirements decrease, but information completeness may be lost
Solution Approach 1:
The system applies different treatment to different instances of duplicate text based on their local context and attributes. Instead of uniformly removing all duplicates, it modifies attributes of duplicate text to create differentiation, preserving information while reducing redundancy. This localized approach maintains data completeness while achieving compression
Data Source
AI summary
A computer-implemented method, including receiving a first file composed of unstructured data with a first file format; applying one or more programmatic solutions for identifying one or more duplicate text in the first file format wherein the one or more duplicate text are associated with one or more attributes contained in unstructured data of the first file; identifying duplicate text contained in the first file wherein the duplicate text is associated with at least a first attribute of the unstructured data; in response to identifying duplicate text contained in the first file format, applying a mechanism for modifying the duplicate text in the first file format to change the first attribute of the unstructured data to a second attribute of a second file format; and converting the unstructured data of the first file into a second file of structured data of the second file format composed of the second attribute.