Data-Bound Chart Objects With Shared Axis Scaling
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Solution Overview
Problem
Existing data visualization techniques require manual drawing or specialized coding, making them time-consuming and resource-intensive, especially when dealing with multiple datasets, and lack intuitive methods for synchronizing properties across visualizations.
Innovation Solution
A system that allows multiple data visualizations to be created and managed on a single digital canvas by binding datasets to graphic objects, enabling synchronization of properties like scale and zero point for consistent representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual drawing or specialized coding is used to create data visualizations, then customization and control are improved, but time consumption and resource intensity increase significantly
Solution Approach 1:
The system enables data visualizations to create themselves automatically by binding datasets to graphic objects. The computer automatically generates multiple graphic objects corresponding to dataset rows and configures their visual properties based on data bindings, eliminating the need for manual drawing while maintaining customization through intuitive binding operations.
Solution Approach 2:
The system performs preliminary configuration by automatically creating graphic objects and establishing their basic structure before the user needs them. When a dataset is bound to a graphic object, the system proactively generates all necessary visual elements and their relationships, so that when data is added or updated, the visualization is already prepared and ready for immediate display.
2Adaptability or versatility
If multiple data visualizations are created on the same digital canvas, then integration and comparison are improved, but property synchronization complexity increases
Solution Approach 1:
The system implements universal property management that works across all data visualizations simultaneously. A single scale binding or zero point adjustment applied to a graphic object automatically propagates to all other graphic objects bound to the same dataset or sharing the same visual property binding, providing consistent control across multiple visualizations without requiring separate configuration for each.
Solution Approach 2:
The system merges the management of multiple data visualizations into a unified framework where graphic objects from different datasets can share common bindings and properties. By combining multiple datasets and their corresponding visualizations under a single canvas with shared binding mechanisms, the system reduces management complexity while maintaining the ability to handle diverse data sources.
3Extent of automation
If data bindings are established between datasets and graphic objects, then automation and consistency are improved, but initial setup complexity increases
Solution Approach 1:
The system uses template-based binding where a single graphic object serves as a template that is automatically copied and configured for each row in the bound dataset. The binding configuration is established once for the template object, and the system automatically replicates this configuration across all generated graphic objects, reducing setup complexity while maintaining high automation.
Solution Approach 2:
The system segments the binding configuration into independent, reusable units that can be applied to multiple graphic objects. By dividing the binding setup into discrete property bindings (such as separate bindings for position, size, color, etc.), the system allows users to configure each property independently and have these segmentations automatically applied across all visualizations, simplifying the overall setup process.
Data Source
AI summary
Embodiments are disclosed for managing multiple data visualizations on a digital canvas. In some embodiments, a method of managing multiple data visualizations includes generating a first graphic object on a digital canvas. A first dataset is received and used to generate a first chart based on the first dataset and a visual property of the first graphic object. The first chart comprises a first plurality of graphic objects including the first graphic object. A second dataset is then received and used to generate a second chart on the digital canvas based on the second dataset. The second chart includes a second plurality of graphic objects. An axis of the first chart and an axis of the second chart are merged such that the axis the first chart and the axis of the second chart share a scale attribute.