Network Risk Estimation Using Tchebychev Polynomial Curves
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Solution Overview
Problem
Current risk analysis methods for IT infrastructure disasters are computationally expensive and require significant RAM, as they involve a full one-to-one analysis of disaster data to estimate the likelihood of future disasters, which is inefficient and time-consuming.
Innovation Solution
A method using Tchebychev polynomials to estimate the risk of future disasters by identifying relationships between previous disasters' severities and times of occurrence, generating a polynomial curve, and modifying it to extract peaks, thereby reducing the number of peaks until a predetermined value is reached, facilitating efficient risk estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full one-to-one analysis of disaster data is performed to estimate future disaster likelihood, then measurement precision is improved, but productivity deteriorates due to computational expense and time consumption
Solution Approach 1:
The patent segments the disaster data analysis by identifying and extracting only the most significant peaks (major disasters) from the complete dataset. Instead of analyzing all disaster events equally, the method divides the data into significant peaks and less significant events, focusing computational resources on the peaks that contribute most to risk estimation. This is achieved through polynomial curve fitting that automatically identifies and extracts peak values representing major disaster events.
Solution Approach 2:
The patent extracts only the essential information needed for risk estimation by identifying peak values from the polynomial curve representing disaster data. Rather than processing the complete disaster dataset, the method extracts peak points that represent significant disaster events and uses these extracted peaks for risk calculation. This extraction process removes unnecessary data processing while preserving the critical information needed for accurate risk assessment.
2Measurement precision
If a full one-to-one analysis of disaster data is performed, then measurement precision is improved, but loss of time worsens due to the time-consuming nature of comprehensive analysis
Solution Approach 1:
The patent applies preliminary action by pre-processing the disaster data through polynomial curve fitting to identify and mark peak locations before the actual risk estimation process. The polynomial curve is fitted to the disaster data in advance, and peak points are pre-identified and extracted. This preliminary processing organizes the data in a way that accelerates subsequent risk calculations, as the system only needs to analyze the pre-identified peaks rather than processing the entire dataset from scratch during risk estimation.
3Reliability
If comprehensive disaster data analysis is performed, then reliability is improved through complete data consideration, but device complexity increases due to computational resource requirements
Solution Approach 1:
The patent changes the parameter representation of disaster data by transforming raw disaster event data into a polynomial curve model. Instead of working with discrete disaster event records, the method transforms the data into continuous polynomial functions where peak values represent significant events. This parameter transformation simplifies the computational model, as risk estimation can be performed by analyzing polynomial peak characteristics rather than processing comprehensive disaster databases, thereby reducing device complexity while maintaining reliability.
Data Source
AI summary
First and second Tchebychev polynomial curves are generated and pass through first and second data points representing previous failures of a first and second computer system of a computer network, respectively. The coordinates of each first and second data point denote a time of occurrence of the failure of the first and second computer system and a severity of the failure of the first and second computer system, respectively. A third Tchebychev polynomial curve passing through the peaks of the first and second Tchebychev polynomial curves is generated. A highest peak of the third Tchebychev polynomial curve is identified and determined to be a high risk failure that occurred on the first or second computer system and generates significant risk to the computer network, causing other computers to add redundancy to minimize the significant risk to the computer network from a future occurrence of the high risk failure.