Indexed Data Format for Reducing Redundant Structure Tags
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Solution Overview
Problem
Current data interchange formats, such as JSON, XML, and CSV, suffer from poor storage optimization due to redundancy in data structure, leading to inefficient data exchange and larger file sizes during transmission.
Innovation Solution
The Indexed Data Object Language (IDOL) format separates data structure from data, using indices to eliminate redundancy by writing the structure only once and mapping data optimally, allowing for efficient serialization and conversion of messages into usable application data or objects, and enabling seamless conversion to and from any data type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data formats (JSON, XML, CSV) are used for data interchange, then data exchange functionality is achieved, but storage optimization is poor and file sizes are large
Solution Approach 1:
The patent segments data into structure information and content information. Structure information (schema, data types, relationships) is extracted and stored separately as a header or metadata, while content information (actual data values) is stored in a compressed format. This segmentation eliminates redundant structure repetition in traditional formats like XML and JSON, significantly reducing file size while maintaining data exchange capability.
Solution Approach 2:
The patent creates a template or schema copy that represents the data structure once, and then references this template multiple times throughout the data. Instead of copying the full structure definition for every data element (as in XML/JSON), the system stores the structure template once and uses references/indices to point to it, reducing redundancy while preserving the complete data exchange functionality.
2Quantity of substance
If data structure is repeated for each data element in traditional formats, then data validation and parsing are simplified, but storage space is wasted and file sizes increase
Solution Approach 1:
The patent divides the data representation into two distinct segments: a structure definition segment (header/schema) and a data content segment. The structure segment contains all necessary validation rules, data types, and relationship definitions, while the content segment stores only the actual values. This segmentation reduces storage space by eliminating structure repetition, while the structured header maintains validation capability.
Solution Approach 2:
The patent performs preliminary structure extraction and validation rule definition before storing the actual data. By pre-defining the data schema, constraints, and relationships in a header section, the system eliminates the need to repeat structure information for each data element. This preliminary action reduces storage requirements while maintaining validation capability during data exchange.
Data Source
AI summary
A computer-implemented method for electronic exchange of data is provided. The method includes the following operations performed by at least one computer processor. These operations include creating source data, identifying data structure from the source data, generating a header file based on the data structure, localizing identical data structure, and storing groups of data that have identical structure in a single data tag.