Eliminating Rounding Error in Iterative Big Data Calculations
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Solution Overview
Problem
Current methods for processing Big Data or streamed data in real-time are hindered by rounding error accumulation during iterative calculations, which can lead to inefficient and costly recalculations, failing to meet the requirements of real-time decision-making, especially in the financial industry.
Innovation Solution
A computing-device-based system that eliminates rounding error accumulation by iteratively calculating a function for a primary computation window while incrementally calculating for backup computation windows, exchanging roles when a backup computation window reaches a pre-defined size, thereby re-initializing components and preventing error accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If iterative calculation is used to process Big Data efficiently, then processing speed and resource utilization are improved, but rounding errors accumulate leading to reduced calculation accuracy
Solution Approach 1:
The patent segments the continuous iterative calculation process into discrete computational windows. Each window processes a specific portion of data independently, allowing the system to maintain calculation accuracy by resetting rounding errors at window boundaries while preserving the efficiency benefits of iterative processing within each segment.
Solution Approach 2:
The patent performs preliminary initialization of computational parameters and data structures before entering iterative calculation. By pre-configuring the computation environment and preparing data in advance, the system eliminates the need for costly recalculations while maintaining accuracy through controlled error resetting at predefined intervals.
2Measurement precision
If initial values are re-calculated to avoid rounding errors, then calculation accuracy is improved, but processing time and computational cost increase
Solution Approach 1:
The patent implements periodic resetting of computational windows at predetermined intervals rather than continuously recalculating initial values. This periodic approach maintains calculation accuracy by clearing rounding errors only when necessary, significantly reducing the time and computational resources required compared to continuous recalibration.
Solution Approach 2:
The patent changes the computational parameters by switching between different window sizes and reset intervals based on data characteristics and processing requirements. This dynamic parameter adjustment allows the system to optimize the balance between accuracy maintenance and processing efficiency for different computational scenarios.
Data Source
AI summary
The present invention extends to methods, systems, and computing system program products for elimination of rounding error accumulation in iterative calculations for Big Data or streamed data. Embodiments of the invention include iteratively calculating a function for a primary computation window of a pre-defined size while incrementally calculating the function for one or more backup computation windows started at different time points and whenever one of the backup computation windows reaches a size of the pre-defined size, swapping the primary computation window and the backup computation window. The result(s) of the function is/are generated by either the iterative calculation performed for the primary computation window or the incremental calculation performed for a backup computation window which reaches the pre-defined size. Elimination of rounding error accumulation enables a computing system to steadily and smoothly run iterative calculations for unlimited number of iterations without rounding error accumulation.