Data Analysis Apparatus Module Scoring and Recommendation
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Solution Overview
Problem
The existing data analysis process is inefficient due to the need for repeated iterations and exponential increases in time and cost, especially in data pre-processing and post-processing steps, and requires manual comparison of various methods to find the optimal analysis approach, which is time-consuming for beginner analysts.
Innovation Solution
A data analysis apparatus and method that generates module combination processes using user-defined data analysis modules, calculates scores based on execution results, and recommends optimal module combinations through a scoring system that includes accuracy and elapsed time analysis, utilizing a back propagation method to update parameter information and select high-scoring modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis processes are used with multiple iterations of pre-processing, analysis model development, and post-processing, then analysis accuracy can be improved, but time consumption and cost increase exponentially
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple module combination processes and evaluating them before the actual data analysis task. The automated module selection and combination generation happen in advance, creating ready-to-use analysis pipelines that eliminate the need for manual iterative experimentation during the actual analysis work
Solution Approach 2:
The system enables self-service by automatically selecting optimal module combinations and generating analysis processes without requiring manual intervention. The automated evaluation and selection mechanisms allow the system to serve itself in optimizing the analysis pipeline, freeing analysts from repetitive manual configuration and comparison tasks
2Reliability
If all possible variable combinations are tested to ensure optimal analysis method selection, then analysis reliability is improved, but the complexity and cost of the process increases exponentially
Solution Approach 1:
The system segments the complex analysis process into distinct, manageable modules that can be independently evaluated and recombined. By breaking down the analysis pipeline into discrete modules with specific functions, the system can systematically test combinations without overwhelming complexity, maintaining reliability through structured module evaluation while avoiding exponential process complexity
Solution Approach 2:
The system creates universal module templates that can serve multiple analysis purposes. Each module is designed with multi-functionality, allowing the same module to be used in different combinations and contexts. This universality reduces the total number of unique modules needed while maintaining the ability to handle diverse analysis requirements reliably
3Measurement precision
If beginner analysts manually compare results through multiple experiments to find optimal analysis methods, then analysis quality can be improved, but productivity decreases due to time-consuming manual work
Solution Approach 1:
The system implements feedback mechanisms that automatically evaluate module combination performance and provide guidance for optimization. The automated evaluation provides immediate feedback on which module combinations yield better results, eliminating the need for manual comparison and allowing analysts to quickly converge on optimal solutions with maintained quality
Solution Approach 2:
The system replaces the mechanical manual process of comparing analysis results with an automated computational system. Instead of analysts manually executing and comparing multiple experiments, the automated system performs the comparisons and selections, dramatically increasing productivity while maintaining or improving analysis quality through more systematic evaluation
Data Source
AI summary
A method performed by a data analysis apparatus according to an embodiment of the present disclosure includes generating a plurality of module combination processes using a plurality of data analysis modules defined by a user, calculating a score for each of the data analysis modules based on an execution result of the plurality of module combination processes and generating a recommendation module candidate group including a combination of data analysis modules selected based on the score.