Adaptive Resolution Histogram for Skewed Data Distribution
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Solution Overview
Problem
Existing methods for determining data distribution in big data sets with non-uniform or skewed distributions are inefficient, particularly for large datasets, as they often require accessing the entire data set or using impractical sample sizes, leading to high processing burdens and inaccurate representations.
Innovation Solution
An adaptive resolution histogram is created using a trie data structure, where node values represent frequency distributions and node positions define keys or key prefixes, allowing for dynamic adjustment of resolution based on key frequencies, enabling efficient data distribution determination while minimizing memory usage and processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sampling techniques are used to generate histograms for big data sets, then processing time and memory usage are reduced, but the accuracy of data distribution representation deteriorates when sample size is insufficient
Solution Approach 1:
The data set is divided into multiple partitions, and histograms are generated for each partition separately. This segmentation allows processing of manageable data portions while maintaining overall distribution accuracy through aggregation of partition-level histograms.
Solution Approach 2:
Different partitions may have different data distribution characteristics. The invention generates histograms with appropriate resolution for each local partition based on its specific skewness and distribution patterns, rather than applying a uniform approach to the entire data set.
2Measurement precision
If a large number of partitions are used to provide sufficient granularity for skewed data distributions, then measurement precision improves, but device complexity and processing burden increase
Solution Approach 1:
The histogram resolution is made dynamic and adaptive based on the actual data distribution characteristics. Partitions with high skewness receive finer granularity histograms, while partitions with uniform distributions use coarser histograms, optimizing the balance between precision and complexity.
Solution Approach 2:
The invention changes the resolution parameter of histograms dynamically based on data skewness metrics. By adjusting histogram bin counts and range based on observed data characteristics, it achieves high precision where needed while reducing complexity in uniform regions.
3Measurement precision
If the entire input data is accessed at once to determine data distribution, then measurement precision improves, but productivity deteriorates due to high memory usage and processing time
Solution Approach 1:
The data set is divided into multiple partitions that can be processed independently and in parallel. This segmentation enables distributed processing across multiple computing nodes, improving productivity while maintaining accuracy through aggregation of partition-level results.
Solution Approach 2:
The invention performs preliminary analysis of data skewness and distribution characteristics during the partitioning phase. This preliminary action enables optimization of histogram parameters before full processing, improving both efficiency and accuracy without requiring complete data loading.
Data Source
AI summary
A method, apparatus, and system for determining a data distribution is provided by using an adaptive resolution histogram. In an embodiment, the adaptive resolution histogram is created using a trie, wherein node values in the trie represent frequency distributions and node positions define associated keys or key prefixes. Keys are derived from input data such as database records that are streamed from a record source. These keys may be processed as received to build the trie in parallel with the production of the input data. To provide adaptive resolution, new child nodes may only be created in the trie when a node value is incremented beyond a predetermined threshold. In this manner, the histogram adjusts the allocation of nodes according to the actual distribution of the data. The completed adaptive resolution histogram may be used for various tasks such as partitioning for balanced parallel processing of the input data.