Automated Analytics System for Feature Selection and Insight Generation
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Solution Overview
Problem
Conventional business intelligence (BI) systems require human intervention and domain expertise for data analysis, leading to biased and time-consuming insights, especially when dealing with large datasets.
Innovation Solution
The implementation of machine learning and artificial intelligence technologies to automate the analysis workflow, enabling automated feature selection, variance analysis, and actionable insights generation without manual analysis or specialized knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning and AI technologies are implemented to automate analysis workflow, then productivity and accuracy of insights are improved, but device complexity increases
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: data preprocessing module, feature selection module using variance analysis, machine learning model training module, and insight generation module. Each module handles a specific aspect of the analysis workflow, making the overall complex system manageable and maintainable while achieving high productivity through automation.
Solution Approach 2:
The patent introduces intermediate processing layers including variance analysis as a mediator between raw data and machine learning models. This intermediary step automatically identifies and selects significant features, reducing the complexity burden on end users while enabling the system to handle large datasets efficiently and generate actionable insights rapidly.
2Measurement precision
If automated feature selection and variance analysis are performed at every level of user analysis, then measurement precision and reliability of insights are improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary variance analysis and feature selection before main machine learning operations. By pre-identifying and selecting significant features using variance thresholds, the system reduces the dimensionality of data entering the ML models, thereby improving measurement precision of feature selection while reducing the computational time and resources required for subsequent analysis stages.
3Adaptability or versatility
If all machine learning algorithms are run at each level of analysis, then adaptability and versatility of the system are improved, but productivity decreases due to redundant computations
Solution Approach 1:
The system implements a tiered approach where not all machine learning algorithms are executed at every analysis level. Instead, variance analysis and feature selection are performed selectively based on data characteristics and user needs. This partial action approach maintains adaptability by allowing algorithm selection where needed while improving productivity by avoiding redundant computations across all levels of analysis.
Data Source
AI summary
A data analysis system is provided. Processing resources are configured to at least: identify features within a dataset, identify potential features of interest therefrom, and enable selection of one of the identified potential features of interest. Responsive to an identified potential feature of interest being selected: (a) algorithms are run on the dataset to identify at least one related feature that the selected feature of interest is most likely and/or most heavily influenced by; (b) a display is generated to include a visual representation of each related feature, each including associated data value representations; and (c) a visual representation can be selected. A data value representation is selectable together with the selected visual representation. Responsive selection of the visual representation, (a)-(c) are repeated. Responsive to a data value representation being selected in (c), the dataset is filtered based on it, and the repetition is performed with the filtered dataset.