Dashboard Controls for Visual Data Manipulation
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Solution Overview
Problem
Existing dashboard systems overwhelm users with numerous interaction controls for visual data manipulation, making it difficult for them to effectively manage and understand the impact of changes on interconnected data elements.
Innovation Solution
A visual data manipulation system that provides 'change points' or 'hot points' within the dashboard, allowing users to interact with data by hovering or selecting elements to see the effects of changes, while employing AI for probabilistic analysis to infer desired actions and dynamically adjust related data elements, and offering personalized and consolidated controls based on user preferences and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If comprehensive controls are provided for visual data manipulation, then functionality and adaptability are improved, but device complexity and ease of operation deteriorate due to overwhelming number of controls
Solution Approach 1:
The system segments the comprehensive set of manipulation controls into context-relevant subsets. Based on the current visual data type and user interaction context, only necessary controls are displayed while others are hidden or aggregated, reducing the visible complexity while preserving full functionality when needed.
Solution Approach 2:
The control interface dynamically adapts its complexity based on user needs and context. The system transitions between simplified and comprehensive control modes, adjusting the number and type of displayed controls in real-time to balance adaptability with ease of operation.
2Adaptability or versatility
If all manipulation controls are displayed, then adaptability is improved, but ease of operation worsens due to user distraction and overwhelming interface
Solution Approach 1:
Different regions or contexts of the interface provide different levels of control detail. Frequently accessed controls are prominently displayed with simple access, while advanced or less frequently used controls are hidden or accessible through contextual commands, creating local variations in interface complexity that enhance overall ease of operation.
Solution Approach 2:
The system automatically determines which controls to display based on analysis of user behavior patterns and current data context. The interface self-adjusts to provide the most relevant controls without requiring users to navigate through overwhelming options, improving ease of operation while maintaining adaptability.
3Productivity
If AI analysis is added to infer user actions, then productivity is improved through automation, but device complexity increases
Solution Approach 1:
An AI analysis layer is introduced as an intermediary between user input and system execution. This intermediary component infers user intent and automatically performs routine manipulation actions, improving productivity by reducing manual intervention while encapsulating the complexity within a dedicated module rather than distributing it throughout the entire system.
Data Source
AI summary
A system (and corresponding methodology) by which a user can interact directly with visual data is provided. The system employs associations and relationships between visual data objects to automatically update objects based upon a change in other objects. The innovation also provides specialized controls (e.g., dashboard tools/controls) that facilitate manipulation of visual data. As there can be numerous manners in which a user can interact with visualization data, the innovation enables a specialized set of controls to be identified and provided to a user thereby reducing overwhelming effects of a large number of controls.