Automated Pre-Generated Plot Repository for Data Exploration
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Solution Overview
Problem
Data scientists spend a significant amount of time performing repetitive and tedious tasks in data exploration, requiring complex programming to generate plots and uncover hidden patterns in large multidimensional datasets.
Innovation Solution
A system that automatically generates pre-plots on any dataset, allowing users to search a repository of pre-generated plots using a simplified interface, ranking and sorting them based on information theoretic metrics to provide relevant results, thereby reducing the need for manual programming and labor-intensive data exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data scientists manually generate plots through complex programming, then plot generation flexibility and customization are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system pre-generates a comprehensive repository of plots by automatically varying plot parameters (chart type, axes, legends, filters) before user queries are received. This preliminary generation of multiple plot variants eliminates the need for users to manually program plot creation, directly reducing time consumption while maintaining plot generation flexibility through the pre-computed repository.
Solution Approach 2:
The system creates copies of standard plot templates and parameter configurations that can be rapidly instantiated for different datasets and queries. These pre-configured plot copies are stored in a repository and can be retrieved or modified with minimal user input, replacing the time-consuming process of manually programming each plot from scratch while preserving customization capabilities.
2Productivity
If automated pre-plot generation is implemented, then time required for data exploration is reduced, but system complexity and computational resources increase
Solution Approach 1:
The system segments the complex plot generation task into independent parameter variations (chart types, axis configurations, legends, filters) that can be generated and stored separately. This segmentation allows the system to manage complexity by breaking down the generation process into manageable components that are pre-computed and stored in a structured repository, enabling efficient retrieval without requiring complex real-time computation.
Solution Approach 2:
By performing plot generation computations in advance before user queries are received, the system shifts the computational burden from real-time processing to pre-computation. This preliminary action reduces the complexity of the user-facing system during actual data exploration operations, as the system only needs to search and retrieve pre-generated plots rather than generate them on-demand.
3Adaptability or versatility
If a comprehensive repository of pre-generated plots is created, then plot variety and coverage are improved, but storage requirements and data management complexity increase
Solution Approach 1:
The system pre-generates plots for a comprehensive range of possible queries and datasets in advance, creating a robust repository that covers various plot types, configurations, and data scenarios. This preliminary creation of diverse plot variants ensures comprehensive coverage before user queries are received, allowing the system to adapt to different data exploration needs without requiring on-demand generation for each unique query.
Solution Approach 2:
The system uses standardized plot templates and parameter configurations that can be copied and instantiated for different datasets and queries. These reusable plot copies are stored in the repository, allowing the system to achieve comprehensive plot coverage by varying existing templates rather than creating entirely new plots for each scenario, thereby managing repository size while maintaining versatility.
Data Source
AI summary
Data exploration as search over automated pre-generated plot objects can include data analytics systems with automated data mining and simplified user experience front ends. A computer-implemented method, that can be performed by the described data analytics systems, includes receiving a request for plots or plot types of a specified criteria; searching a plot object resource for plots relevant to the specified criteria, the plot object resource comprising an indexed repository of available plots; sorting and ranking the plots or plot types according to associated scores, the associated scores for each plot being based on information theoretic metrics relevant to a measure of interest; and providing plots satisfying a criteria of the sorting and the ranking to a source of the request.