Log-Linear Bins for Time-Series Histogram Precision
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Solution Overview
Problem
Conventional systems face inefficiencies in storing and retrieving time-series histogram data, often exceeding visual space limits, leading to inefficient data visualization and transmission of indistinguishable values.
Innovation Solution
The implementation of log-linear bins using two significant digits base 10 for storing and generating histograms, allowing for consistent binning across different data streams, enabling efficient aggregation and compression while maintaining precision and visual clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear bins are used for histogram data, then measurement precision is maintained, but the quantity of data stored and transmitted exceeds visual space requirements
Solution Approach 1:
The patent transforms the binning parameter from linear to log-linear scale, where bins are distributed logarithmically across the data range. This parameter change allows the same measurement precision to be achieved with fewer bins, reducing the quantity of histogram data while maintaining the ability to distinguish meaningful variations in the data.
Solution Approach 2:
The system dynamically adjusts the number and distribution of bins based on the range and distribution of the input data values. By using log-linear binning, the system adapts to different data scales and ranges, optimizing the balance between measurement precision and data quantity for each specific dataset.
2Measurement precision
If more bins are used to maintain precision, then measurement precision improves, but the complexity of data storage and processing increases
Solution Approach 1:
By changing from linear to log-linear binning parameters, the patent reduces the total number of bins required to achieve the same measurement precision. This parameter transformation simplifies the data storage structure and reduces processing complexity while maintaining the ability to accurately represent data distributions across different scales.
3Ease of operation
If data is compressed to fit visual space, then visual clarity improves, but measurement precision deteriorates
Solution Approach 1:
The log-linear binning parameter transformation enables the system to achieve both visual clarity and measurement precision simultaneously. The logarithmic distribution of bins provides better resolution in lower value ranges while still representing higher values, allowing the data to be visualized effectively without losing measurement precision.
Data Source
AI summary
A method for constructing histograms may include receiving a set of data values, wherein the data values are two-dimensional or three-dimensional and the data values contain at least one measurement over time; creating a plurality of bins in memory, wherein the plurality of bins are log-linear bins using two significant digits base 10; creating a zero bin representing the number 0; placing non-zero numbers of the set of data values into the plurality of bins; and generating one or more histograms using the bins.