Dynamic Histogram Buckets for Streaming Data Precision
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Solution Overview
Problem
Existing data mining methods struggle to create accurate histograms from incomplete or streaming data sets, as they require a complete data set for representation, which is not always available, especially when dealing with large volumes of data where precision is needed only in specific ranges.
Innovation Solution
A system and method for creating a histogram from a data stream by specifying internal buckets with heterogeneous differences between minimum and maximum values, allowing for varying granularities based on user interest, where data elements are populated and outputted in a way that maintains finer precision near the focus and coarser precision farther away, enabling efficient data representation without storing uninteresting data with high precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complete data set is stored with full precision for all values, then the histogram can be constructed accurately, but the memory and processing requirements increase significantly
Solution Approach 1:
The patent applies local quality by maintaining high precision only for data values within the user-specified range of interest, while using lower precision for values outside this range. This is achieved through heterogeneous bucketing where different precision levels are applied to different value ranges, reducing overall memory requirements while preserving accuracy where needed.
Solution Approach 2:
The patent segments the data range into multiple precision levels. A first precision level is used for values within the user-specified range of interest, while a second, lower precision level is used for values outside this range. This segmentation allows the system to allocate memory resources efficiently based on user needs.
2Measurement precision
If high precision is maintained for all data values, then the histogram representation is more accurate, but the processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by maintaining high precision only for data values within the user-specified range of interest, while using lower precision for values outside this range. This is achieved through heterogeneous bucketing where different precision levels are applied to different value ranges, reducing overall processing requirements while preserving accuracy where needed.
3Device complexity
If uniform precision buckets are used across the entire data range, then the histogram construction is simpler, but the representation is less useful when user interest is concentrated in specific ranges
Solution Approach 1:
The patent applies local quality by maintaining high precision only for data values within the user-specified range of interest, while using lower precision for values outside this range. This is achieved through heterogeneous bucketing where different precision levels are applied to different value ranges, reducing overall processing requirements while preserving accuracy where needed.
Solution Approach 2:
The patent implements a dynamic bucketing structure where the precision level applied to data values depends on whether they fall within the user-specified range of interest. This dynamic approach allows the histogram to adapt to user needs, providing high precision where required and lower precision elsewhere, rather than using a static uniform precision approach.
Data Source
AI summary
A method for creating a histogram from a plurality of data elements that includes specifying a plurality of internal buckets, wherein each internal bucket of the plurality of internal buckets represent values between an internal minimum value and an internal maximum value, wherein a plurality of differences of the internal minimum value and the internal maximum value of each internal bucket are heterogeneous, populating the plurality of internal buckets with the plurality of data elements based on the internal minimum value and the internal maximum value of each internal bucket to obtain a plurality of populated internal buckets, and outputting the histogram from the plurality of populated internal buckets.