Multi-Variable Data Generation Using Binning and Contingency Tables
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Solution Overview
Problem
Existing data generation processes struggle with efficiently generating new data from complex data sets with multiple variable types, often focusing on a single variable or a small set and failing to accurately replicate the characteristics and patterns of the original data.
Innovation Solution
A system that splits an original data set into subsets of continuous and discrete data types, converts continuous data into discrete data through dimension reduction and binning, and generates a new data set using contingency tables to mimic the original data's patterns and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data generation focuses on a single variable or small set of variables, then the process is simple and fast, but the accuracy and representativeness of generated data deteriorates
Solution Approach 1:
The patent segments the data generation process by separating continuous and discrete variables into different processing paths. Continuous variables are processed through dimensionality reduction and binning, while discrete variables are processed through contingency table generation. This segmentation allows each variable type to be handled optimally, maintaining both speed and accuracy.
Solution Approach 2:
The patent applies parameter changes by transforming continuous data into discrete data through binning operations. The system adjusts parameters such as bin width and number of bins to preserve the statistical characteristics of the original data while enabling efficient processing. This transformation maintains data accuracy while improving generation speed.
2Reliability
If the system processes all variable types simultaneously, then the data generation is comprehensive, but the computational complexity increases
Solution Approach 1:
The system segments the processing complexity by handling continuous and discrete variables separately. Continuous variables undergo dimensionality reduction followed by binning, while discrete variables are processed through contingency table generation. This segmentation reduces the computational burden on any single processing step while maintaining comprehensive data representation.
Solution Approach 2:
The patent applies preliminary action by performing dimensionality reduction on continuous variables before binning. This preliminary step simplifies the data structure and reduces the computational complexity of subsequent binning operations, making the overall process more manageable while preserving data representativeness.
3Productivity
If continuous data is converted to discrete data through binning, then the data processing becomes more efficient, but the information loss increases
Solution Approach 1:
The system carefully adjusts binning parameters such as bin width and number of bins to minimize information loss. By optimizing these parameters, the system maintains the statistical characteristics of the original continuous data while achieving efficient discrete processing. The parameter optimization balances processing efficiency with information preservation.
Solution Approach 2:
The patent implements feedback mechanisms to evaluate the quality of binned data and adjust the binning parameters accordingly. This feedback loop ensures that the binning process maintains sufficient information content while achieving the desired processing efficiency. The system iteratively refines binning parameters based on data quality metrics.
Data Source
AI summary
An example operation may include one or more of storing an original data set in memory, splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set, converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation, generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set, and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.