Extracting Signal Differences from Sparse Data Sets
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Solution Overview
Problem
Analyzing and comparing sparse data sets is challenging due to their low average values and high number of null values, leading to significant memory and processing requirements, as well as high rates of false positives and negatives, making it difficult to extract significant signals.
Innovation Solution
Normalization and separation of data sets using a MapReduce function to reduce the impact of large values, identify correlations, and extract statistically significant differences without extensive entity-by-entity comparisons, thereby reducing memory footprint and processor requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual comparisons between many thousands or millions of entity values are performed, then accurate signal detection may be achieved, but significant memory and processing time, as well as processor-memory bandwidth, are consumed
Solution Approach 1:
The patent segments the data set into multiple partitions or blocks, allowing comparisons to be performed on smaller subsets of data rather than requiring all entity values to be loaded and processed simultaneously. This segmentation reduces the memory footprint and processor-memory bandwidth requirements while maintaining the ability to detect significant signals through aggregated statistical analysis across partitions.
2Measurement precision
If individual comparisons between many thousands or millions of entity values are performed, then accurate signal detection may be achieved, but significant memory and processing time are consumed
Solution Approach 1:
The patent segments the data set into multiple partitions or blocks, allowing comparisons to be performed on smaller subsets of data rather than requiring all entity values to be loaded and processed simultaneously. This segmentation reduces the memory footprint and processor-memory bandwidth requirements while maintaining the ability to detect significant signals through aggregated statistical analysis across partitions.
Solution Approach 2:
The patent performs partial comparisons by focusing computational resources on identifying and analyzing only those entity values or partitions that show statistically significant differences, rather than performing exhaustive comparisons across all entity values. This approach maintains detection accuracy for significant signals while improving processing efficiency by avoiding unnecessary computations on non-significant data.
3Loss of information
If average values across a region are calculated in sparse data sets, then overall trends may be identified, but the large number of intervening null values reduces average values and makes it difficult to extract significant signals
Solution Approach 1:
The patent extracts and excludes null values from the averaging calculation, computing averages only over non-null entity values within a region. This extraction approach preserves the ability to identify overall trends while preventing the dilution of average values by excessive null values, thereby maintaining the significance of detected signals in sparse data sets.
Solution Approach 2:
The patent changes the calculation parameter from a simple arithmetic mean that includes null values to a modified average or statistical measure that is computed only over non-null values. This parameter change ensures that the presence of numerous null values does not artificially reduce the average and obscure significant signals in the data.
Data Source
AI summary
The present disclosure provides systems and methods for extracting signal differences from sparse data sets. Data sets for comparison, including a control data set and one or more test data sets, may be normalized and separated into subsets or groupings via a MapReduce function. Normalization may account for large values present in both control and test data sets that would otherwise reduce the significance of smaller correlated values, creating false negatives. The MapReduce may provide identification and analysis of correlations between sets via related entities. Accordingly, via the systems and methods discussed herein, a computing device may extract statistically significant differences between data sets, without requiring extensive entity by entity comparison (or entity to every entity comparison, which, for a data set of millions of entities, may be too computationally expensive or take too long), reducing memory footprint and processor requirements.