Sparse Data Signal Extraction for Low-Load Analytical Processing
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Solution Overview
Problem
Processing large sets of sparse data, such as genetic variant databases, is resource-intensive and inefficient, requiring significant computational power, storage space, and bandwidth due to the presence of noise and redundancy, making it difficult to extract relevant signals effectively.
Innovation Solution
A method and system for extracting relevant signals from sparse data sets by comparing data values to predefined criteria, collecting additional data when necessary, and discarding irrelevant data, thereby reducing processing requirements and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force scanning approach is used to detect hidden signals, then signal detection capability is improved, but computational power and bandwidth requirements increase extensively
Solution Approach 1:
The patent extracts hidden signals from sparse data sets by identifying and isolating relevant data points that contain the concealed information. This is achieved through comparative analysis across multiple data sets, where the system identifies patterns or anomalies that indicate the presence of hidden signals without requiring exhaustive scanning of all data, thereby reducing computational overhead while maintaining detection capability.
2Loss of information
If entire data sets are processed to extract relevant signals, then completeness of signal extraction is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of data sets before full analysis. By pre-identifying potential signal locations through comparative methods and establishing initial criteria for relevance, the system prepares the data in advance, allowing the main extraction process to focus only on promising candidates rather than processing every data point exhaustively, thus reducing overall processing time while maintaining extraction completeness.
3Measurement precision
If sophisticated machine learning algorithms are used for signal extraction, then extraction accuracy is improved, but computational power and storage space requirements increase significantly
Solution Approach 1:
The patent employs lightweight, computationally efficient algorithms that can be executed rapidly and discarded after use, replacing the need for heavy, persistent machine learning models. By using simpler comparative analysis methods that require minimal computational resources and storage space, the system achieves acceptable extraction accuracy without the burden of maintaining complex models, effectively using disposable analytical approaches for each extraction task.
Data Source
AI summary
The methods discussed herein can extract relevant signals from sparse data sets, for instance in cryptographic analysis, noise reduction, pattern recognition, or computational genetics. The present solution can improve technological performance of an analytical device such as through reducing server load, computation time, and data storage sizes. The present solution can identify relevant signals, such as genetic variants with a high probability of pathogenicity, in large, sparse data sets.