Branching Pattern Extraction for Event Sequence Visualization
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Solution Overview
Problem
Conventional sequence analysis systems fail to provide meaningful insights due to the high volume and complexity of event sequence data, leading to inadequate visualization techniques that hinder informed decision-making for website managers and software developers.
Innovation Solution
The system efficiently extracts and visualizes event sequence data by identifying key events and event sequence flow paths using a rank-divide-trim methodology, generating interactive visualizations that represent the frequency of event sequences, allowing for intuitive understanding of user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional sequence analysis systems process large event sequence datasets, then comprehensive data coverage is achieved, but meaningful insight extraction fails due to high volume and complexity
Solution Approach 1:
The patent extracts only the most frequent events (key events) from the large event sequence dataset, rather than processing all events. This extraction approach filters out noise and focuses on significant patterns, enabling meaningful insight extraction while maintaining comprehensive data coverage of important user behaviors.
Solution Approach 2:
The patent creates a simplified copy of the event sequence data in the form of a branching pattern visualization. This visualization copy represents the essential structure and flow paths of user interactions without requiring processing of the entire raw dataset, thus preserving meaningful insights while reducing computational burden.
2Loss of information
If conventional visualization techniques are used to illustrate event sequence data, then data representation is provided, but effective analysis is hindered due to volume and complexity
Solution Approach 1:
The patent segments the complex event sequence data into a hierarchical branching pattern structure with nodes representing key events and branches representing flow paths. This segmentation organizes the data in a visually manageable way that maintains comprehensive representation while enabling effective analysis through intuitive visualization of user interaction patterns.
Solution Approach 2:
The patent transforms the flat, high-volume event sequence data into a multi-dimensional branching pattern visualization. This dimensional transformation projects complex sequential data into a visual space that preserves relationships and frequencies while making the data easily analyzable through spatial arrangement and visual hierarchy.
3Quantity of substance
If conventional systems process millions of events with thousands of sequences, then complete dataset analysis is achieved, but computational efficiency decreases
Solution Approach 1:
The patent extracts only the essential elements (key events and their frequencies) from the complete dataset, rather than processing all millions of events. This extraction maintains dataset completeness for important patterns while dramatically improving computational efficiency by focusing resources on significant data points.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of the data required to generate meaningful insights. Instead of exhaustively analyzing all event sequences, it identifies and processes the most frequent events and their flow paths, achieving sufficient analytical depth with reduced computational effort.
Data Source
AI summary
The present disclosure is directed toward systems and methods for extracting a branching pattern from a dataset of event sequences. For example, one or more embodiments described herein extract a branching pattern from a dataset that illustrates patterns of events within the dataset. Additionally, one or more embodiments described herein generate one or more interactive visualizations based on the extracted branching pattern that enable an analyst to query specific portions of the extracted branching pattern.