Visualization Recommendation Engine for Data Object Selection
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Solution Overview
Problem
Users face difficulty in determining suitable measures and dimensions for data visualizations in conventional database systems, making it challenging to create effective visual representations of data.
Innovation Solution
A system that includes a visualization server, metadata, and clients, which provides users with a user interface to select objects and calculates a 'relatedness' value based on usage data to recommend appropriate objects for inclusion in visualizations, using a formula to determine the relevance of unselected objects to currently selected ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select business objects to create visualizations, then they have full control over the selection process, but it becomes difficult to determine suitable measures and dimensions to include
Solution Approach 1:
The system analyzes usage data from multiple users to generate recommendations for suitable measures and dimensions. This feedback mechanism provides users with information about which business objects are commonly used together, helping them make informed selections without manually analyzing all possible combinations.
Solution Approach 2:
The patent introduces an intermediary recommendation system that acts as a mediator between the user and the database schema. This intermediary analyzes usage patterns and suggests appropriate business objects, reducing the cognitive burden on users while maintaining their control over the final selection.
2Ease of operation
If the system provides comprehensive guidance on object selection, then users can easily determine suitable measures and dimensions, but the system complexity increases
Solution Approach 1:
The system automatically analyzes usage data and generates recommendations without requiring manual configuration or complex setup. The recommendation engine serves itself by continuously learning from user interactions, reducing the operational complexity despite providing comprehensive guidance.
Solution Approach 2:
The patent copies usage patterns from multiple users to generate recommendations. Instead of implementing complex domain knowledge, the system replicates successful selection patterns observed in actual usage, simplifying the recommendation mechanism while providing comprehensive guidance.
3Measurement precision
If the system tracks and analyzes usage data to generate recommendations, then it can provide accurate suggestions, but the data processing requirements increase
Solution Approach 1:
The usage data collected serves multiple purposes: it not only generates recommendations but also improves the overall system performance, enables personalized user experiences, and supports continuous learning. This multi-functionality justifies the data processing requirements by extracting maximum value from the collected information.
Data Source
AI summary
A system includes reception of a selection of a first object from a plurality of measure objects and dimension objects, determination, for each of a plurality of unselected objects of the plurality of measure objects and dimension objects, of a first number of visualizations which are associated with the first object and with the unselected object, determination of a second number of visualizations which are associated with the first object, determination, for each of the plurality of unselected objects, of a third number of visualizations which are associated with the unselected object, determination of a total number of visualizations, and determination, for each of the plurality of unselected objects, of a value associated with the unselected object based on the first number associated with the unselected object, the second number, the third number associated with the unselected object and the total number.