Event Sequence Data Visualization Matrices
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Solution Overview
Problem
Conventional event sequence data analysis visualization tools, such as Sankey diagrams, become cumbersome and difficult to read as websites become more complex and heavily trafficked, failing to provide accurate representations of sequence data and comparisons between datasets.
Innovation Solution
The system generates matrices representing transitions between events in a sequence, arranged in a zig-zag pattern with visual indicators to show event occurrences, allowing for clear and easy understanding of complex data, including identification of popular nodes and transitions through histogram statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Sankey diagrams are used to visualize event sequence data, then transitions between webpages can be illustrated, but the visualization becomes difficult to read accurately as the website becomes more complex and heavily trafficked
Solution Approach 1:
The patent segments the complex event sequence data into multiple sequential matrices, where each matrix represents a specific time step or phase in the user navigation sequence. This segmentation divides the overwhelming bulk data into manageable, interpretable units that can be processed and understood incrementally, resolving the contradiction between handling large traffic volumes and maintaining readability.
Solution Approach 2:
The patent introduces a temporal dimension by arranging matrices in sequence to represent the progression of user navigation over time. This transforms the static Sankey diagram into a dynamic multi-dimensional visualization where the fourth dimension (time) is explicitly represented through the sequence of matrices, allowing accurate representation of complex traffic patterns without compromising readability.
2Quantity of substance
If conventional visualization tools are used for heavily trafficked websites, then all user transitions can be captured, but accurate representation of sequence data becomes difficult
Solution Approach 1:
By segmenting the complete event sequence into discrete time-step matrices, the patent maintains completeness of data while improving measurement precision. Each matrix captures transitions at a specific moment with clear temporal context, allowing accurate reconstruction of the complete user journey through sequential analysis of individual matrices rather than overwhelming simultaneous data.
Solution Approach 2:
The patent performs preliminary organization of event data into structured matrices before visualization, pre-processing the raw clickstream data into a format that preserves sequence accuracy. This preliminary structuring ensures that when the data is visualized, the temporal relationships and transition sequences are already optimized for accurate representation.
3Loss of information
If detailed event sequence data is visualized, then comprehensive analysis is possible, but the visualization becomes cumbersome and difficult to understand
Solution Approach 1:
The patent segments comprehensive event data into a sequence of simpler matrices, each representing a manageable time step. This segmentation preserves all necessary analysis information while reducing the visual complexity at any given moment, allowing analysts to understand detailed sequences without being overwhelmed by the entire dataset displayed simultaneously.
Solution Approach 2:
The patent resolves the complexity issue by distributing information across multiple dimensions - spatial arrangement of matrices and temporal sequencing. This multi-dimensional approach organizes detailed information in a structured framework that reduces perceived complexity while maintaining complete analytical capability.
Data Source
AI summary
The present disclosure is directed toward systems and methods for analyzing event sequence data. Additionally, the present disclosure is directed toward systems and methods for providing visualizations of event sequence data analyses. For example, systems and methods described herein can analyze event sequence data related to websites and provide matrix-based visualizations of the event sequence data. The matrix-based visualization can be interactive and can allow a user to trace changes in traffic volume across webpages and hyperlinks of a website.