Price Change Data Compression via Range Segmentation
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Solution Overview
Problem
Existing methods for validating and processing financial product prices consume significant data processing and storage resources due to the large volume of raw historic price data, necessitating more efficient compression techniques.
Innovation Solution
A computer system calculates price change values and categorizes them into ranges, assigning codes from a code table to represent these changes, allowing for efficient storage and processing by reducing data volume while retaining relevant information for validation and other purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If raw historic price data is stored and processed for price validation, then price validation accuracy is improved, but data storage requirements and processing resources increase significantly
Solution Approach 1:
The patent segments price change data into discrete categories based on predefined ranges (e.g., small, medium, large price changes). Each price change value is mapped to a category code, transforming continuous data into segmented discrete representations. This segmentation reduces data volume while preserving validation accuracy because the categories are designed to capture the essential characteristics needed for price validation.
Solution Approach 2:
The patent changes the parameter representation from raw price change values to categorical codes. By transforming the data from one parameter representation (numeric price changes) to another (categorical codes), the system reduces storage requirements while maintaining the information needed for validation. The code table maps category codes to price change ranges, preserving the semantic meaning while compressing the data.
2Reliability
If raw historic price data is processed for price validation, then validation reliability is improved, but data processing resources and time increase significantly
Solution Approach 1:
The patent segments price change data into discrete categories, which simplifies processing operations. Instead of processing continuous numeric values, the system processes categorical codes that require less computational effort. This segmentation maintains validation reliability while significantly improving processing efficiency because category comparisons are faster than raw value analyses.
Solution Approach 2:
The patent transforms the data parameter from raw price change values to categorical codes, changing the nature of processing required. This parameter transformation reduces computational complexity and processing time while maintaining validation reliability, as the categorical representation preserves the essential information needed for validation decisions.
3Quantity of substance
If price change data is compressed into categories, then data storage requirements are reduced, but information loss may occur
Solution Approach 1:
The patent applies parameter changes by transforming price change values into categorical codes based on predefined ranges. This transformation is designed to preserve critical information for validation while reducing storage requirements. The category definitions are carefully constructed to maintain the semantic meaning and validation relevance of price changes, minimizing information loss.
Solution Approach 2:
The patent extracts only the essential information needed for price validation from the raw price change data. By taking out and retaining only the categorical information relevant for validation (rather than storing all raw detail), the system reduces storage requirements while maintaining validation accuracy. The extraction process selectively preserves the most important characteristics for validation purposes.
Data Source
AI summary
A computer system compresses value change data. Value change ranges are identified for value changes corresponding to different times. Codes are selected based on the identified value change ranges. The ranges are adjusted in response to the occurrence of a stop condition. The value change range codes are concatenated with machine-coded categories and stored.