Dynamic Data Visualization via Correlation Analysis
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Solution Overview
Problem
Existing data visualization systems require extensive manual efforts and domain knowledge to generate customized visualizations, leading to labor-intensive processes and suboptimal human-computer interaction, as they are preconfigured with a fixed set of graphical plots and interfaces, limiting the ability to dynamically adapt to new data sources and user interactions.
Innovation Solution
A data visualization platform that calculates correlation coefficients across time-series data and incorporates user search queries to dynamically generate new visualizations, using an analysis engine, correlation engine, and feedback engine to determine which metrics and interfaces to display, reducing the need for manual updates and enhancing user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the application is preconfigured with a fixed set of graphical plots and interfaces, then the application structure is simple and stable, but the adaptability to new data sources and user needs deteriorates
Solution Approach 1:
The application dynamically generates visualizations and interfaces based on incoming data characteristics and user interactions, rather than using fixed preconfigured plots. The system adapts its structure in real-time by calculating correlation measures and automatically creating appropriate visual representations, making the application both adaptable and manageable through automated processes
Solution Approach 2:
The system performs self-configuration by automatically analyzing new data sources, calculating correlation coefficients between metrics, and generating appropriate visualizations without requiring manual developer intervention. The application serves itself by adapting its interface structure based on data characteristics and user behavior patterns
2Productivity
If manual updates are required for each new data source, then the visualization quality is controlled and precise, but the time and labor required deteriorates
Solution Approach 1:
The system pre-calculates correlation measures between metrics when data is first ingested and maintains this information for quick access. When new visualizations are needed, the system leverages these pre-computed correlation measures to rapidly generate appropriate plots without requiring time-consuming manual analysis or developer intervention
Solution Approach 2:
The system incorporates user interaction feedback to continuously improve visualization generation. By monitoring which visualizations users view and interact with, the system learns from user behavior and automatically adjusts its visualization selection and generation strategy, improving productivity over time while reducing the need for manual updates
3Ease of operation
If the application uses fixed interfaces, then the user interface consistency is maintained, but the user interaction quality deteriorates
Solution Approach 1:
The system captures and utilizes user interaction information by monitoring which visualizations users view, how long they spend on each visualization, and what actions they take. This feedback is fed back into the system to automatically adjust the generated visualizations and interfaces, improving ease of operation while preserving and utilizing user behavior information to enhance the interaction experience
Data Source
AI summary
Described embodiments provide systems and methods for generating visualizations of data based on correlation measures and search history. An analysis engine may access data observed from a data source over time. The analysis engine may determine a variation of each of at least a first metric and a second metric of the data, over time. A correlation engine may determine a correlation measure between the first metric and a second metric, over time. The correlation engine may generate, responsive to the correlation measure being greater than a reference level, a visualization of the first metric and the second metric varying in time, on a device to display to a user.