Data Object Compression Using Offset Optimization Constraints
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Solution Overview
Problem
Existing data management systems face challenges in efficiently reducing the size of data object sets without losing valuable information, and in managing network traffic and processing loads due to excessive data transmission, particularly in financial transactions like foreign exchange (FX) trades.
Innovation Solution
A system and method for data object compression and reduction via multi-purpose optimization, which combines data set compression and data link reduction to maximize size and risk reduction while maintaining information integrity, using a customized processor to adjust data objects and incorporate optimization constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce data set sizes, then storage requirements and processing loads are reduced, but information integrity may be compromised
Solution Approach 1:
The system extracts and removes redundant data objects from the data set while preserving essential information. The optimization procedure identifies and eliminates duplicate or unnecessary data objects, achieving compression by taking out only the redundant portions rather than compressing all data uniformly.
Solution Approach 2:
The system changes the parameters of data objects by adjusting their properties or representations to reduce size while maintaining information content. This includes modifying data structures, changing encoding formats, or transforming data representations to achieve more efficient storage without losing critical information.
2Adaptability or versatility
If data transmission is increased to maintain data availability across systems, then data accessibility is improved, but network bandwidth consumption increases
Solution Approach 1:
The system creates and distributes copies of essential data objects to multiple computing systems, enabling local access without requiring continuous network transmission. Once data is copied to appropriate systems, it can be accessed locally, reducing ongoing network bandwidth consumption while maintaining data availability.
3Productivity
If computing resources are increased to handle processing demands, then processing capability is improved, but hardware costs and system complexity increase
Solution Approach 1:
The system extracts and removes redundant data objects from the data set while preserving essential information. The optimization procedure identifies and eliminates duplicate or unnecessary data objects, achieving compression by taking out only the redundant portions rather than compressing all data uniformly.
Solution Approach 2:
The optimization procedure serves multiple functions simultaneously: it compresses data sets, reduces network traffic, eliminates redundant data objects, and maintains information integrity. This multi-functional approach achieves various goals with a single unified process rather than requiring separate systems for each function.
Data Source
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AI summary
A system for data object compression and reduction includes a processor, a memory coupled with the processor, and first through fifth logic stored in the memory and executable by the processor to cause the processor to obtain a set of data objects from a plurality of data sources, each data object of the set of data objects specifying a data object type, a size, a polarity, and identification data, to obtain optimization constraint data for each data source of the plurality of data sources, to identify those data objects of the plurality of data objects for which the identification data matches, to implement, in accordance with the obtained optimization constraint data, an optimization procedure configured to determine an optimal set of adjustments to the set of data objects that maximizes reduction of both a data set aggregate magnitude and a data link composite magnitude for at least one pair of the plurality of data sources, the optimal set of adjustments including an offset of multiple data objects of the identified data objects of same data object type and opposite polarity, and to store data indicative of the optimal set of adjustments to the set of data objects. The data set aggregate magnitude is indicative of a sum of the size of each data object of the set of data objects, and the data link composite magnitude is indicative of a sum of the sizes of those data objects of the set of data objects linked to the pair of data sources.