Data Sampling Accuracy Maintenance via Statistical Threshold Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data sampling methods in data fabric architectures face challenges in maintaining accuracy, particularly due to large data sizes that result in high overheads on data sources, networks, and application environments, and fail to account for updates to the original data set, leading to degraded sampling data accuracy.
Innovation Solution
A computer-implemented method that continuously monitors and evaluates the accuracy of sampling data by comparing statistics of an original database with those of a sampling database, initiating updates when the sampling database diverges beyond a predetermined threshold, ensuring the sampling data remains representative of the original data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data sampling is used to reduce data size for analysis, then processing time and resource overhead are reduced, but accuracy of data analysis deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing statistics about the original data set before sampling occurs. These statistics include data characteristics, distribution patterns, and key metrics that are captured in advance. When the sampling database is created, these pre-collected statistics are used to guide the sampling process and subsequently validate whether the sample accurately represents the original data, thereby maintaining analysis accuracy while enabling efficiency gains from reduced data size
Solution Approach 2:
The system implements feedback by continuously comparing statistics from the original data set with statistics from the sampling database. When discrepancies exceed a predetermined threshold, the system triggers an update to recreate the sampling database. This feedback loop ensures that the sampling data remains accurate and representative, resolving the contradiction between using small samples for efficiency and maintaining accuracy for reliable analysis
2Loss of energy
If sampling database is not updated after original data changes, then resource overhead is reduced, but sampling data accuracy deteriorates
Solution Approach 1:
The system applies periodic action by scheduling regular comparisons between original data statistics and sampling database statistics at predetermined intervals. This periodic validation ensures that the sampling database remains synchronized with the original data without requiring continuous monitoring. When changes in the original data exceed the threshold, the system triggers an update, thereby maintaining reliability while avoiding unnecessary resource consumption during periods when the data remains stable
3Measurement precision
If full data set is analyzed instead of sampled data, then analysis accuracy is improved, but processing time and resource overhead increase
Solution Approach 1:
The system creates a sampling database that serves as a representative copy of the original data set. This copy contains carefully selected samples that mirror the statistical properties, distribution patterns, and key characteristics of the full data set. By analyzing this smaller copy instead of the complete original data, the system achieves processing time reductions while maintaining analysis accuracy through the representative nature of the sampled data
Data Source
AI summary
According to an aspect, a computer-implemented method includes collecting statistics of an original database and collecting statistics of a sampling database that includes a subset of the original database. The statistics of the original database are periodically updated. The statistics of the original database and the statistics of the sampling database are periodically compared to determine whether the sampling database is within a predetermined threshold of the original database. In response to determining that the sampling database is not within the predetermined threshold of the original database, an update to the sampling database is initiated.