Shared Visualization Library for Cloud Metrics
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Solution Overview
Problem
Manual design of visualizations for cloud computing metrics requires specialized knowledge, leading to disjointed and non-uniform aesthetics across multiple visualizations.
Innovation Solution
A visualization builder generates visualizations based on a received identifier, retrieving data sets and style configurations from a shared visualization library via a backend API, ensuring consistent design elements across workspace, homepage, dashboard, and report visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual design of visualizations is performed, then specialized knowledge and customization are utilized, but disjointed and non-uniform aesthetics result across multiple visualizations
Solution Approach 1:
The visualization design is segmented into reusable style configuration objects that can be independently defined and then applied across multiple visualizations. This allows customization at the component level while maintaining consistency through shared style definitions.
Solution Approach 2:
Style configuration objects serve multiple functions by being reusable across different visualization types and contexts. A single style configuration can be applied to multiple visualizations, ensuring aesthetic consistency while allowing customization through different style selections.
2Manufacturing precision
If manual design of visualizations is performed, then specialized programming knowledge is required, but this increases complexity and time consumption
Solution Approach 1:
Pre-defined style configuration objects act as templates that can be copied and applied to multiple visualizations. This eliminates the need to manually program design elements for each visualization, reducing complexity while maintaining design quality.
Solution Approach 2:
The system provides self-service capabilities through automated retrieval and application of style configurations. Visualizations automatically apply consistent styling without requiring manual programming intervention, reducing the barrier to entry while maintaining professional quality.
3Ease of operation
If manual design of visualizations is performed, then customization is possible, but this results in non-uniform aesthetics across visualizations
Solution Approach 1:
Style configuration objects serve as universal templates that can be applied across different visualization types. This enables designers to maintain aesthetic uniformity by reusing the same style configurations while still allowing flexibility in selecting different styles for different contexts.
Solution Approach 2:
The system allows local customization through style configuration objects that can be selectively applied to specific visualizations or groups of visualizations. This enables fine-tuned control over aesthetics, maintaining uniformity where needed while allowing local variations where appropriate.
Data Source
AI summary
Visualizations are automatically generated based at least on a received identifier identifying a report. One or more data sets and one or more style configurations are retrieved from a shared visualization library via a backend API based on the received identifier and passed to a router component. The router component includes a switch that renders a score component, which generates a score visualization, if the retrieved data sets include score data. If the data sets include time series data, or the score visualization also includes a time series graph, the router component renders a time series component that passes the data sets and the style configurations to a parser that parses the data sets and the style configurations and outputs one or more highcharts options objects. A highcharts component generates the time series visualizations and/or adds the time series graph to the score visualization based on the highcharts options objects.