Iterative Covariance Calculation for Streamed Data
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Solution Overview
Problem
Current methods for processing and analyzing Big Data, particularly streamed data, are inefficient and resource-intensive, leading to slow response times and significant waste of computing resources due to the need to recalculate statistical calculations for large data sets in real-time applications like financial trading and online gaming.
Innovation Solution
The method involves iteratively calculating covariance for streamed data by adjusting computation windows and recalculating components based on previous values, reducing the need to revisit all data elements and minimizing redundant computations, thereby enhancing efficiency and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional covariance calculation methods are used on streamed data, then calculation accuracy is maintained, but computational time and resource consumption increase significantly
Solution Approach 1:
The patent pre-calculates and stores intermediate statistical components (sums, means, products) as data elements flow through the computation window. These pre-computed values are maintained in running totals that can be quickly adjusted when data elements are added or removed from the window, eliminating the need to recalculate from scratch and significantly reducing computational time while maintaining accuracy.
Solution Approach 2:
The covariance calculation is decomposed into separate intermediate components (sum of x, sum of y, sum of products, counts) that can be independently tracked and updated. This segmentation allows each component to be maintained separately through the stream processing pipeline, enabling efficient incremental updates without reprocessing all data elements.
2Reliability
If traditional covariance calculation methods are used on streamed data, then complete data analysis is achieved, but computing resource consumption increases significantly
Solution Approach 1:
The system pre-computes and maintains running totals of intermediate statistical components as data streams through the computation window. These pre-maintained values enable rapid covariance updates without requiring full data reprocessing, significantly reducing CPU cycles and energy consumption while ensuring complete and accurate data analysis through the maintenance of all necessary statistical components.
Solution Approach 2:
The patent implements continuous tracking of statistical components (sums, means, products, counts) as data elements flow through the computation window. This continuous maintenance of intermediate values ensures that the system is always ready to calculate covariance immediately when needed, without interruption or reprocessing, thereby reducing overall computing resource consumption while maintaining analysis completeness.
3Measurement precision
If all data elements in computation windows are repeatedly accessed for covariance recalculation, then calculation accuracy is maintained, but buffer access efficiency decreases
Solution Approach 1:
The patent pre-calculates intermediate statistical components (sums of data elements, sums of products, counts) and maintains them in running totals as data flows through the computation window. This preliminary computation eliminates the need to repeatedly access and re-sum all data elements in the buffer for each covariance calculation, significantly improving buffer access efficiency while maintaining statistical accuracy through the preserved intermediate values.
Data Source
AI summary
The present invention extends to methods, systems, and computing system program products for iteratively calculating covariance for Big Data. Embodiments of the invention include iteratively calculating one or more components of a covariance for two modified computation subsets based on one or more components of a covariance for two previous computation subsets and then calculate the covariance for two modified computation subsets based on the iteratively calculated components. Iteratively calculating covariance avoids visiting all data elements in the modified computation subsets and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.