Event Chain Visualization with Nested Performance Metrics
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Solution Overview
Problem
Current event chain visualizations, such as Gantt charts, lack an effective way to represent complex performance data from multiple sequences of a process, making it difficult to illustrate relationships and trends in resource utilization and performance metrics over time.
Innovation Solution
An event chain visualization that uses bars or shapes on a timeline, connected by relationships, with sub-graphs and additional indicators like color, shading, and sparklines to show aggregated and detailed performance data, allowing for a comprehensive view of process steps and data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional Gantt charts are used to visualize process data, then the visualization is simple and easy to understand, but it cannot effectively represent complex performance data from multiple sequences and relationships
Solution Approach 1:
The patent embeds multiple levels of information within the timeline bars by nesting sub-graphs, sparklines, and additional data visualizations inside the main event chain bars. This allows complex performance data from multiple sequences to be contained within the simple Gantt chart structure, resolving the contradiction by preserving information completeness while maintaining the familiar simple visualization format.
Solution Approach 2:
The patent adds vertical dimensionality to the traditional horizontal timeline by incorporating sub-graphs, color coding, shading patterns, and sparklines that display additional performance metrics vertically within or alongside the timeline bars. This multi-dimensional approach enables comprehensive representation of complex performance data without overwhelming the user with a completely new visualization paradigm.
2Loss of information
If multiple data sets from multiple sequences are aggregated in one visualization, then comprehensive performance overview is achieved, but it becomes difficult to distinguish individual sequence relationships and trends
Solution Approach 1:
The patent applies different visual properties (colors, shading patterns, line styles) to different sequences and data sets within the same visualization. Each sequence or data type has its own distinctive visual characteristics, allowing users to easily distinguish between multiple sequences while viewing them aggregated in one comprehensive timeline view. This resolves the contradiction by maintaining both completeness and interpretability.
Solution Approach 2:
The patent segments the aggregated performance data into distinct visual components including separate timeline bars for different sequences, sub-graphs for specific metrics, and sparklines for trend indicators. This segmentation allows users to parse the comprehensive data set into manageable, interpretable units while still viewing the complete picture, thus resolving the contradiction between comprehensive overview and ease of interpretation.
3Measurement precision
If detailed performance metrics are displayed for each event, then comprehensive analysis capability is provided, but the visualization becomes cluttered and hard to read
Solution Approach 1:
The patent places detailed performance metrics in sub-graphs and sparklines that occupy vertical space or side positions rather than expanding the horizontal timeline. This dimensional reorganization allows detailed metrics to be displayed without cluttering the main timeline view, resolving the contradiction by providing comprehensive measurement precision while maintaining visual readability through spatial organization.
Solution Approach 2:
The patent nests detailed performance metrics, sub-graphs, and additional data within the timeline bar structures themselves. This nesting allows comprehensive performance data to be contained within the compact timeline visualization without creating external clutter, resolving the contradiction by providing detailed measurement precision while maintaining a clean, readable overall structure.
Data Source
AI summary
An event chain visualization of performance data may show the execution of monitored elements as bars on a timeline, with connections or other relationships connecting the various bars into a sequential view of an application. The visualization may include color, shading, or other highlighting to show resource utilization or performance metrics. The visualization may be generated by monitoring many events processed by an application, where each bar on a timeline may reflect multiple instances of a monitored element and, in some case, the aggregated performance.