Real-Time User Behavior Analysis Using Trigger Log Segmentation
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Solution Overview
Problem
Current methods for analyzing user behavior on web servers only provide statistical numerical values and cannot detect specific user behaviors or abnormalities in real time, limiting their effectiveness for system management and marketing applications.
Innovation Solution
An apparatus and method that collect log data in real time, detect trigger logs, generate cumulative log data, and perform pattern analysis to identify user behavior, allowing for real-time analysis and detection of abnormal activities and enabling user-customized product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If statistical numerical values are provided using log data, then system management and marketing analysis are supported, but specific user behavior analysis and abnormal behavior detection are not possible
Solution Approach 1:
The patent segments log data into distinct event types (click, view, purchase, etc.) and analyzes each event type separately to identify specific user behaviors. This segmentation enables detailed behavior analysis without requiring complete reconstruction of the entire analysis system.
Solution Approach 2:
The patent pre-defines behavior patterns and rules for analyzing user actions before actual log data arrives. By establishing analysis frameworks in advance, the system can quickly match incoming log data against predefined patterns, enabling specific behavior detection without complex real-time processing.
2Speed
If real-time log data collection and analysis is implemented, then user behavior can be analyzed immediately, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential and relevant features from log data for analysis, rather than processing all available data. By focusing on key behavioral indicators and filtering out redundant information, the system achieves real-time analysis with reduced computational overhead.
Solution Approach 2:
The patent applies partial action by analyzing only specific event types or user segments that are most relevant to current analysis goals, rather than processing all log data uniformly. This selective approach enables faster processing while maintaining analysis effectiveness.
3Measurement precision
If detailed pattern analysis is performed on cumulative log data, then specific user behavior patterns can be detected, but processing complexity and time increase
Solution Approach 1:
The patent pre-establishes behavior patterns and analysis rules before processing log data. By having predefined patterns ready, the system can quickly match incoming cumulative log data against these patterns without performing complex real-time pattern generation, thus maintaining high detection accuracy with reduced processing time.
Solution Approach 2:
The patent adjusts analysis parameters such as time windows, event thresholds, and pattern matching sensitivity dynamically based on data characteristics and analysis requirements. This parameter optimization enables accurate behavior detection while minimizing processing time and computational resources.
Data Source
AI summary
Method and apparatus for analyzing an online behavior of a user accessing a web server include: collecting, when a user terminal accesses a web server and forms a session, log data corresponding to a behavior performed by the user terminal in the session in real time; detecting log data corresponding to a trigger log among the log data; extracting, when the trigger log is detected, log data cumulated up to a detection time point of the trigger log from a start time point of the session and generating cumulative log data; and performing pattern analysis on the cumulative log data and generating behavior information corresponding to the behavior of the user terminal.