Tabular Structured Data Format Conversion Arrays
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Solution Overview
Problem
There is a need for a generic solution to convert data between structured formats (such as JSON, YAML, XML) and tabular formats (such as XLS) due to the relative benefits of each format in different environments, with structured data being easier for modern tools and services to process and tabular data being more convenient for manipulation and filtering.
Innovation Solution
A method and system that converts data between tabular and structured formats by building arrays based on identified headers, parsing data rows, and storing data in structured or tabular formats using governing arrays to map data correctly, allowing for conversion from tabular to structured and vice versa.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in structured format (JSON, YAML, XML), then it is easier for modern tools and services to process, but it is less convenient for manipulation and filtering compared to tabular format
Solution Approach 1:
The patent segments the conversion process into distinct phases: parsing the structured data format, mapping hierarchical structures to tabular rows and columns, and generating the tabular output. This segmentation allows the system to handle complex nested structures by breaking them down into manageable mapping operations, thereby enabling conversion between formats with different operational characteristics
Solution Approach 2:
The patent introduces an intermediary mapping layer that translates between structured format hierarchies and tabular format structures. This intermediary mapping mechanism serves as a mediator that preserves the processing advantages of structured formats while providing the manipulation convenience of tabular formats, allowing users to work in the most suitable format for each task
2Ease of operation
If data is stored in tabular format (XLS), then it is more convenient for manipulation and filtering, but it is less suitable for processing by modern tools and services compared to structured format
Solution Approach 1:
The patent implements bidirectional conversion capability, allowing conversion from structured to tabular format and vice versa. This inversion approach enables data to be transformed into the most appropriate format depending on the task: tabular format for manipulation and filtering operations, and structured format for processing by modern web services and tools
3Adaptability or versatility
If a generic conversion solution is implemented between tabular and structured formats, then compatibility and usability in different environments are enhanced, but the complexity of the conversion system increases
Solution Approach 1:
The patent implements a universal conversion framework that handles multiple structured data formats (JSON, YAML, XML) and tabular formats through a single unified system. The conversion engine uses standardized mapping rules and configuration files to accommodate different format types, reducing the need for separate conversion tools for each format pair and managing complexity through abstraction
Data Source
AI summary
Aspects of the present disclosure relate to converting between structured and tabular data formats. Data can be received in a tabular format. An array can be built for each of a plurality of objects within the data in the tabular format, each object corresponding to at least one identified header of the identified headers. A data row can be parsed using at least one of the built arrays and data within the data row can be added to the structured format in a specific location based characteristics indicated in the at least one array. Data can also be converted from the structured format into the tabular format using the built arrays.