Representative Data Object Compression for Swap Data Histories
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Solution Overview
Problem
Existing computing systems face challenges in managing large data sets due to limited resources and the need for intelligent data compression that preserves valuable information, particularly in financial applications like interest rate swaps, where data objects with different start dates are not efficiently compressed, leading to increased storage and processing burdens.
Innovation Solution
The method involves defining representative data objects that group variable constituent data objects with different histories, allowing for optimal compression and reduction of data sets without information loss, using techniques like coupon blending and multilateral compression, which can automatically process data objects and evaluate them for compression based on risk constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce storage and processing requirements, then storage capacity and processing power are improved, but data loss or reduction in information quality may occur
Solution Approach 1:
The patent changes the parameter of data representation by converting multiple data objects with different start dates into a single aggregated data object with cumulative values. This transformation allows compression while preserving the essential information through mathematical aggregation (summing cash flows, notional amounts, and other quantitative parameters) rather than simple deletion or approximation.
Solution Approach 2:
The patent merges multiple data objects representing financial instruments with different start dates into a single representative data object. By combining similar instruments (e.g., interest rate swaps, futures) that share common characteristics (currency, instrument type, end date) into one aggregated object, the system achieves compression while maintaining the cumulative economic value and risk profile of the original set.
2Reliability
If traditional compression methods are used that require common start dates, then data integrity is maintained, but compression efficiency is reduced
Solution Approach 1:
The patent introduces dynamic grouping criteria that adapt to the data characteristics. Instead of requiring static common start dates, the system dynamically identifies groups based on flexible parameters including currency, instrument type, end date, and optional start date ranges. This dynamic approach allows the compression algorithm to efficiently process diverse financial instruments while maintaining integrity through risk-based validation.
Solution Approach 2:
The patent changes the parameter requirements for grouping by removing the strict common start date constraint and replacing it with flexible parameter matching (currency, instrument type, end date). This parameter transformation enables compression of data objects with different start dates while maintaining data integrity through cumulative aggregation and risk constraint validation.
3Productivity
If data objects with different start dates are compressed together, then compression efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent segments the compression process into distinct phases: grouping data objects by common parameters (currency, instrument type, end date), calculating cumulative values for each group, and validating against risk constraints. This segmentation reduces processing complexity by breaking down the complex task of compressing heterogeneous data objects into manageable, systematic steps that can be efficiently executed.
Solution Approach 2:
The patent simplifies processing complexity by changing the approach from date-based matching to parameter-based grouping. By using fixed parameters (currency, instrument type, end date) and optional start date ranges, the system creates clearly defined groups that are easier to process than traditional methods requiring exact start date matches, thereby improving compression efficiency without excessive complexity.
4Loss of information
If more detailed data is retained to preserve information quality, then information accuracy is improved, but storage requirements increase
Solution Approach 1:
The patent creates composite data objects that combine multiple individual data objects into a single representative object containing aggregated values. This composite structure preserves the essential information (cumulative cash flows, notional amounts, risk metrics) while eliminating redundant storage of individual instrument details, thereby reducing storage requirements while maintaining information accuracy for risk management and settlement purposes.
Data Source
AI summary
A system implements data compression for a plurality of data objects each having a respective fixed data constituent and a variable data constituent. The data compression includes selecting a first subset of the fixed data constituents and a second subset of the variable data constituents. The second subset of the variable data constituents having an end date in common and event timing in common. The system compresses the first subset of the fixed data constituents and the second subset of the variable data constituents by defining a representative data object for the fixed data constituent subset and the variable data constituent subset.