Dashboard Template Descriptor for Dataset Visualization
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Solution Overview
Problem
Users face difficulties in selecting the most appropriate dashboard or dashboard template to visualize their datasets due to a lack of consideration for the datasets used with existing templates and how they were utilized in visualizations, leading to suboptimal data representation.
Innovation Solution
A computer-implemented method and system that extracts column-to-visualization mappings, concept combinations, and high-level topics from dashboards, aggregating this information into a dashboard template descriptor stored in a database to facilitate the selection of the most suitable dashboard template for visualizing user datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing dashboard templates are recommended based on popularity and style preferences, then users can quickly find templates, but the templates may not be appropriate for the user's specific dataset and visualization needs
Solution Approach 1:
The system performs preliminary analysis of datasets to extract concepts, statistics, and relationships before template recommendation. This includes identifying column concepts, computing statistical attributes, and detecting concept combinations in advance, so that templates can be matched based on these pre-computed features rather than just popularity metrics
Solution Approach 2:
The patent replaces manual template selection and basic popularity-based recommendation with an automated semantic analysis system. The system automatically extracts concepts from datasets, analyzes statistical relationships, and matches these with template descriptors containing concept combinations, substituting human judgment and simple popularity metrics with sophisticated automated analysis
2Ease of manufacture
If dashboard templates are created without considering dataset characteristics, then template creation is simplified, but the templates fail to accurately represent the data
Solution Approach 1:
The system performs preliminary analysis of datasets to extract concepts, statistics, and relationships before template creation or recommendation. This includes identifying column concepts, computing statistical attributes, and detecting concept combinations in advance, so that templates can be matched or created based on these pre-computed features
Solution Approach 2:
The system enables templates to self-describe their data requirements through automatically generated descriptors. The template descriptor contains extracted concept combinations, topics, and column-to-visualization mappings that allow the template to independently communicate its compatibility with specific datasets, eliminating the need for manual annotation
Data Source
AI summary
A computer-implemented method, system and computer program product for creating a descriptor for a dashboard template. The column-to-visualization mappings are extracted from a dashboard of a created or modified dashboard (or a created or modified dashboard template). Furthermore, the concept combinations from each visualization of the dashboard are extracted. Additionally, topics from the dashboard are extracted. The concept combinations, topics and column-to-visualization mappings are aggregated into a dashboard template descriptor. The dashboard template descriptor is then stored. In this manner, the dashboard template descriptor captures how concept combinations are used in the visualizations of the dashboard as well as how high-level concepts (topics) are incorporated in the dashboard. Furthermore, the dashboard template descriptor captures how the concepts of the columns of a dataset are mapped to the visualizations of the dashboard. As a result, the most appropriate dashboard/dashboard template may be selected to visualize the user's dataset.