Filter Propagation Across Linked Data Models
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Solution Overview
Problem
Existing data visualization systems struggle to effectively integrate and filter data from multiple data models, limiting the ability to present comprehensive insights by not propagating filters across linked data models.
Innovation Solution
A system that provides visualizations from different data models, allowing users to associate attributes between models and apply filters, which are then propagated across linked data models, enabling synchronized data updates and enhanced visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple data models are integrated in existing visualization systems, then comprehensive data presentation is achieved, but filter propagation across linked data models is not implemented, limiting comprehensive insights
Solution Approach 1:
The system implements feedback by detecting filter applications on one visualization and automatically propagating those filters to related visualizations through associated data models. This feedback loop ensures that filtering actions ripple through the entire data model network, maintaining consistency and enabling comprehensive insights across all linked visualizations without requiring manual intervention.
2Ease of operation
If filters are applied to individual visualizations, then specific data subsets are obtained, but the filters are not propagated to other visualizations from different data models, limiting synchronized data updates
Solution Approach 1:
The system introduces an intermediary mechanism that acts as a bridge between different data models and visualizations. When a filter is applied to one visualization, the intermediary detects this action, translates it into appropriate filter criteria for associated data models, and propagates it to other visualizations. This intermediary layer maintains stability and synchronization across the system while preserving ease of operation through automatic filter propagation.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program provides a first visualization that includes a first set of data from a first data model. The program further provides a second visualization that includes a second set of data from a second data model. The program also receives an association between a first attribute in the first data model and a second attribute in the second data model. The program further receives a filter on the first set of data from the first data model. The program also applies the filter on the first visualization. The program further propagates the filter to the second visualization based on the association.