Query-Based Data Visualization Preference Tracking
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Solution Overview
Problem
Conventional reporting tools lack the ability to determine the preferred visualization for a user based on individual preferences, leading to suboptimal presentation of result sets.
Innovation Solution
The system stores user-selected visualization information for specific queries and query patterns, using this data to determine the most appropriate visualization to present based on the user and query received, by maintaining a metadata structure that associates users, queries, and visualization types with counters to track user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional reporting tools present visualizations without considering user preferences, then the system is simple to operate, but the visualization may not align with user preferences reducing effectiveness
Solution Approach 1:
The system automatically determines and applies user preferences without requiring manual configuration. Users simply interact with the reporting tool normally, and the system autonomously tracks their visualization selections and applies learned preferences to future queries, eliminating the need for users to manually set up preference configurations while ensuring personalized visualization delivery
Solution Approach 2:
The system implements a feedback loop where user interactions with visualizations are tracked and stored as preference data. This feedback information is then used to automatically adjust future visualization selections, creating a continuous improvement cycle where the system learns from user behavior patterns and adapts its recommendations accordingly
2Reliability
If the system tracks and stores user preference data for each query and visualization type, then visualization effectiveness is improved, but device complexity increases
Solution Approach 1:
The preference tracking mechanism serves multiple functions: it stores user preferences, analyzes query patterns, determines appropriate visualizations, and provides feedback for continuous improvement. By making the preference storage system multi-functional, the patent reduces the need for separate dedicated components for each function, thereby managing complexity while achieving effective personalized visualization
Solution Approach 2:
The system manages complexity by dynamically adjusting the level of preference tracking based on user behavior patterns and query characteristics. Rather than uniformly tracking all possible parameters for all users, the system adapts its data collection and processing based on actual usage scenarios, reducing overhead while maintaining effective personalization
Data Source
AI summary
A system includes reception of a first query from a first user, identification, in response to reception of the first query, of a first plurality of data entries, each of the first plurality of data entries associating the first user, the first query and a respective visualization type with a respective counter value, determination of one of the first plurality of data entries associated with a greatest respective counter value of the counter values of the first plurality of data entries, determination of a respective visualization type of the one of the first plurality of data entries, and presentation of a visualization of the determined visualization type of a first result set corresponding to the first query.