Semi-Structured Data Compression Using Indexed Element Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The inefficient storage and access of semi-structured data, which lacks a database schema and often results in suboptimal storage and transmission efficiency, particularly for data types like XML and JSON.
Innovation Solution
A data compression method that identifies unique elements within semi-structured data, assigns unique indexes, generates encoded representations, and serializes these into compressed files across multiple data stores, enabling efficient storage and transmission by utilizing a data compression module that can operate sequentially or in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If semi-structured data is stored without database schema, then data flexibility and logical organization are improved, but storage efficiency and access performance deteriorate
Solution Approach 1:
The patent segments semi-structured data into distinct elements and assigns unique indexes to each element. This segmentation allows the data to maintain its flexible, schema-less nature while enabling efficient storage and access through indexed references, resolving the contradiction between data flexibility and storage efficiency.
Solution Approach 2:
The patent creates encoded representations (copies) of semi-structured data elements with unique indexes. These encoded copies enable efficient storage and retrieval without requiring the original data structure to be rigidly defined, thus maintaining flexibility while improving storage efficiency.
2Adaptability or versatility
If semi-structured data is stored without database schema, then data organization flexibility is improved, but access performance deteriorates
Solution Approach 1:
The patent performs preliminary actions by assigning unique indexes to data elements during the storage phase. This preliminary indexing enables rapid access during retrieval operations without requiring the data to be pre-structured according to a fixed schema, thus improving access performance while maintaining organizational flexibility.
3Ease of manufacture
If traditional storage methods are used for semi-structured data, then implementation simplicity is maintained, but compression ratio deteriorates
Solution Approach 1:
The patent changes the representation parameters of semi-structured data by encoding elements with unique indexes and storing them in a standardized format. This parameter transformation enables significant compression ratios while maintaining implementation simplicity through systematic encoding and decoding processes.
Data Source
AI summary
A method for compressing semi-structured data is discussed. The method includes accessing semi-structured data, the semi-structured data comprising a plurality of elements. The method includes determining a plurality of unique elements of the plurality of elements, each of the plurality of unique elements associated with a respective unique index of a plurality of unique indexes. Each of the unique index can indicate a position in one of a plurality of data stores. The method includes generating a sequence of encoded representations corresponding to the plurality of elements, the generating based on the plurality of unique indexes.