Context-Aware Insight Evaluation for Data Visualization
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Solution Overview
Problem
Existing data visualization techniques fail to accurately present insights required by users due to misalignment with user context, as they rely on uniform template data that does not account for varying user needs and data contents.
Innovation Solution
An information processing apparatus and method that calculates relevance between context data and evaluation dataset elements, evaluates insight subjects, and displays information relevant to user insights, using processors to obtain, calculate, and display insights based on user context and dataset relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If uniform template data is used for data visualization, then the visualization process is simplified and automated, but the visualization results do not accurately reflect user context and requirements
Solution Approach 1:
The system dynamically generates multiple candidate visualizations by varying template parameters based on data characteristics and user context, rather than using a fixed uniform template. This allows the visualization process to adapt automatically to different scenarios while maintaining automation.
Solution Approach 2:
The system changes template parameters such as chart type, aggregation method, and grouping based on the evaluated relevance to user context. By dynamically adjusting these parameters according to data features and contextual information, the system maintains automation while improving accuracy.
2Reliability
If multiple evaluation metrics are used to assess insight subjects, then the evaluation comprehensiveness is improved, but the computational complexity increases
Solution Approach 1:
The evaluation process is segmented into multiple independent metrics (relevance to user context, data coverage, visual clarity, etc.), each calculating a specific aspect of insight quality. This modular approach allows comprehensive evaluation while managing complexity through independent, focused calculations for each metric.
3Measurement precision
If relevance calculation between context data and dataset elements is performed, then the precision of insight identification is improved, but the processing time increases
Solution Approach 1:
The system calculates relevance for a selected subset of candidate visualizations rather than all possible combinations. By performing partial relevance calculations on the most promising candidates identified through template parameter variation, the system achieves sufficient precision without exhaustive processing.
Data Source
AI summary
To display information providing an insight required by a user, provided is an information processing apparatus (1) including: an obtaining section (11) that obtains an evaluation dataset and context data; a relevance calculation section (12) that calculates a relevance between the context data and a constituent element of the evaluation dataset; an evaluation section (13) that carries out evaluations of a plurality of insight subjects generated with reference to the evaluation dataset and the relevance; and a display section (14) that displays information relevant to the insight subjects.