Sparse Data Signal Extraction to Reduce Compute and Storage
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Solution Overview
Problem
Processing large sparse data sets, such as genetic variant databases, is resource-intensive due to the need for significant computational power, storage space, and bandwidth, and often requires extensive human intervention, making it inefficient 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 computational and storage requirements.
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 system performs preliminary actions by collecting data from multiple sparse data sets before the actual signal detection process. By pre-collecting and organizing data from multiple sources, the system reduces the computational burden during the detection phase, avoiding the need for extensive brute force scanning while maintaining signal detection capability.
Solution Approach 2:
The system merges data from multiple sparse data sets into a consolidated data structure. By combining data from multiple sources that lack certain identifiers, the system creates a more complete data set that enables signal detection without requiring exhaustive scanning of individual data sets, thus reducing computational power and bandwidth requirements.
2Reliability
If entire data sets are processed to extract relevant signals, then completeness of analysis is improved, but processing time and resource consumption increase
Solution Approach 1:
The system extracts only the necessary data elements from sparse data sets by identifying and collecting data corresponding to specific identifiers that are missing or incomplete. Instead of processing entire data sets, the system extracts only the relevant portions needed for signal detection, maintaining analysis completeness while significantly reducing processing time and resource consumption.
Solution Approach 2:
The system segments the data processing task into distinct phases: identifying missing identifiers, collecting corresponding data from multiple sources, and analyzing only the relevant consolidated data. This segmentation allows the system to avoid processing irrelevant data while ensuring complete analysis of signal-relevant information.
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 system uses self-service mechanisms by automatically identifying missing identifiers and collecting corresponding data from multiple sparse data sets without requiring complex machine learning algorithms. The systematic approach of matching identifiers and consolidating data enables accurate signal extraction through straightforward data processing operations, reducing the need for computationally intensive machine learning models.
4Reliability
If manual curation of databases is performed, then data quality is improved, but human intervention time and cost increase
Solution Approach 1:
The system replaces manual human intervention with an automated computational mechanism that systematically identifies missing identifiers, collects corresponding data from multiple sparse data sets, and consolidates the information. This mechanical substitution maintains high data quality through systematic validation while eliminating the time-consuming nature of manual curation.
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.