Representative Data Objects for Historical Data Compression
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Solution Overview
Problem
Computing resources, such as memory and storage, are depleted due to increased data processing, leading to communication bottlenecks and storage challenges, necessitating efficient data compression that maintains valuable information without loss.
Innovation Solution
The method involves defining representative data objects for groups of data objects, allowing for intelligent compression by allocating fixed and variable constituent data objects into groups with common properties, and using optimization procedures to minimize the number of data objects, thereby reducing storage and communication loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce storage and communication loads, then storage capacity and communication bandwidth are improved, but data accuracy and information completeness may deteriorate
Solution Approach 1:
The patent segments data objects into fixed constituent data objects and variable constituent data objects. Fixed constituents represent stable, reusable components that can be compressed efficiently, while variable constituents capture dynamic changes. This segmentation allows compression without losing essential information, as each segment serves a specific functional purpose in representing the original data.
Solution Approach 2:
The patent creates representative data objects that copy only the essential characteristics and constituents of original data objects. Instead of storing complete duplicates, the system copies only the fixed constituents and key variable constituents that are necessary to reconstruct or represent the original data, achieving compression while maintaining data accuracy for essential information.
2Productivity
If the number of data objects is reduced through compression, then processing speed and system efficiency are improved, but the ability to maintain detailed information may worsen
Solution Approach 1:
By segmenting data into fixed and variable constituents, the system processes only essential fixed constituents repeatedly and handles variable constituents only when changes occur. This segmentation enables faster processing by avoiding redundant operations on unchanged data while preserving complete information through the structured representation of both fixed and variable elements.
Solution Approach 2:
The patent extracts and separates fixed constituents from variable constituents in data objects. Fixed constituents that remain unchanged across multiple data objects are extracted and stored once, while variable constituents are extracted and stored only when they differ. This extraction reduces processing overhead while maintaining complete information about both stable and dynamic aspects of the data.
3Quantity of substance
If intelligent compression is applied to remove redundant data, then storage efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing data objects into fixed and variable constituents with clear definitions and identification rules. This structured segmentation provides a systematic approach to compression that is easier to implement and maintain compared to complex algorithms, as the fixed constituents can be identified and processed using straightforward criteria while variable constituents handle the remaining complexity in a organized manner.
Data Source
AI summary
A system includes first logic to obtain a fixed constituent data object and a variable constituent data object for each data object of a set of objects, second logic to analyze the fixed constituent data objects to allocate each fixed constituent data object to one of a plurality of fixed groups, third logic to analyze the variable constituent data objects to allocate each variable constituent data object to one of a plurality of variable groups, fourth logic to determine a net magnitude for each fixed group and for each variable group, fifth logic to determine a historical component for each variable constituent data object, sixth logic to determine a net historical magnitude for each variable group based on the historical components, and seventh logic to define at least one representative data object to represent the fixed and variable constituent data objects in each pair of fixed and variable groups having a matching common set of properties, the at least one representative data object maintaining the net magnitudes and maintaining the net historical magnitude.