Distributed Data Transformation System for High-Dimensional Datasets
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Solution Overview
Problem
High dimensionality and low signal-to-noise ratio in modern datasets hinder interactive variable-by-variable analysis and transformation, and current meta-learning systems fail to effectively address multiple data quality issues simultaneously due to their reliance on individual data quality metrics, neglecting interactions between them.
Innovation Solution
A system that performs automatic variable analysis and grouping by defining transformation flows and computing phase internal parameter values for each variable and transformation phase, allowing for simultaneous application of multiple data transformations in a minimal number of data passes, thereby enabling effective visualization and transformation of datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If interactive variable-by-variable analysis and transformation is performed, then data quality issues can be addressed, but the process becomes prohibitively time-consuming and inefficient for high-dimensional datasets
Solution Approach 1:
The patent segments the data transformation process into multiple phases (e.g., phase 1, phase 2, etc.) where each phase applies specific transformations to subsets of variables. This allows systematic handling of high-dimensional data without requiring exhaustive variable-by-variable analysis, thereby reducing time loss while maintaining data quality assessment precision.
Solution Approach 2:
The patent performs preliminary actions by pre-defining transformation flows and computing phase internal parameter values before actual data transformation. This includes pre-identifying variables with data quality issues (high missing rates, skewness) and preparing transformation strategies in advance, which significantly reduces the time required during the actual transformation process while maintaining thorough data quality assessment.
2Device complexity
If current meta-learning systems use individual data quality metrics, then the system complexity remains low, but the systems fail to capture interactions between data quality metrics and retain sufficient information
Solution Approach 1:
The patent merges multiple individual data quality metrics into a unified transformation flow framework that captures interactions between metrics. By combining metrics such as missing rate, cardinality, skewness, and other data quality measures into an integrated analysis, the system retains sufficient information about data quality interactions while managing complexity through structured phase-based processing.
Solution Approach 2:
The patent creates a composite information structure by integrating multiple data quality metrics into a comprehensive transformation strategy. This composite approach combines individual metric assessments (missing rate, cardinality, skewness) into a unified transformation flow that captures their interactions, thereby retaining sufficient information without proportionally increasing system complexity.
3Manufacturing precision
If multiple data transformations are applied sequentially in traditional systems, then each transformation can be carefully executed, but the number of data passes increases significantly
Solution Approach 1:
The patent merges multiple transformation operations into a unified transformation flow that can be executed in a minimal number of data passes. By combining phase 1, phase 2, and other transformation phases into an integrated process, the system maintains transformation accuracy while significantly improving data processing efficiency by reducing the total number of passes required.
Solution Approach 2:
The patent ensures continuity of useful action by designing transformation flows where multiple transformations are applied continuously in a single data pass rather than sequentially in separate passes. This continuous transformation approach maintains the precision of each transformation step while maximizing productivity by eliminating redundant data loading and processing overhead.
Data Source
AI summary
A computing system transforms variable values in a dataset using a transformation flow definition applied in parallel. The transformation flow definition indicates flow variables and transformation phases to apply to the flow variables. A computation is defined for each variable and for each transformation phase. A phase internal parameter value is computed for each defined computation from observation vectors read from the dataset. A current variable, a first variable value, a first transformation phase, the phase internal parameter value, and a current transformation phase are selected based on an observation vector read from the dataset. A result value is computed by executing the transformation function with the phase internal parameter value and the first variable value. The computed result value is output to a transformed input dataset. The process is repeated for each variable, transformation phase, and observation vector.