Representative Data Objects for Historical Compression
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Solution Overview
Problem
Computing resources, such as memory and storage, are depleted due to increased data processing, leading to constraints in existing hardware and software infrastructure, particularly in electronic communication systems, necessitating improvements in data management like intelligent compression to maintain valuable information while reducing data size.
Innovation Solution
The method involves defining representative data objects for groups of data objects, allowing for compression by reducing the number of data objects, incorporating variable constituent data objects with different histories into optimization processes, and implementing accrual of historical components to increase compression efficiency, applicable in financial applications like coupon blending and multilateral compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is implemented to reduce storage and processing loads, then memory usage and computing resource requirements are reduced, but data quality and information value may be compromised
Solution Approach 1:
The patent segments data objects into constituent components, identifying and retaining only those constituents that contain valuable information. This segmentation allows selective compression that removes redundant data while preserving information value, directly resolving the contradiction between reducing data volume and maintaining information quality.
Solution Approach 2:
The patent changes the parameters of data representation by transforming detailed data objects into compressed representations that maintain essential characteristics. Through parameter optimization and selective retention, the system achieves data compression while preserving the information value needed for processing, thus resolving the contradiction between quantity reduction and quality maintenance.
2Productivity
If additional computing resources are added to handle increased data processing, then processing capability and memory availability are improved, but system complexity and infrastructure costs increase
Solution Approach 1:
The patent extracts and removes redundant and unnecessary data from the system before processing occurs. By taking out only the essential information needed for processing, the system reduces the data volume that requires computing resources, thereby improving processing capability without adding infrastructure complexity.
Solution Approach 2:
The patent applies partial compression actions selectively to data objects, compressing only when and where it provides benefit. This partial application of compression techniques optimizes processing capability for critical data while avoiding unnecessary complexity in handling all data uniformly, thus improving productivity without proportionally increasing system complexity.
3Adaptability or versatility
If data is communicated between computing systems for processing, then collaborative processing and data sharing are enabled, but network bandwidth consumption and communication time increase
Solution Approach 1:
The patent performs preliminary compression of data objects before they are communicated between computing systems. By pre-compressing data to retain only essential information, the system enables effective data sharing while significantly reducing the time and bandwidth required for communication, thus resolving the contradiction between adaptability and communication time.
4Quantity of substance
If traditional compression methods are applied to data objects, then data size is reduced, but compression efficiency is limited due to inability to handle variable constituent data objects with different histories
Solution Approach 1:
The patent introduces dynamic handling of variable constituent data objects by tracking their histories and relationships. This dynamic approach allows the compression system to adapt to different data types and their temporal relationships, significantly improving compression efficiency compared to static traditional methods while maintaining reduced data size.
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.