User Session Log Analysis via PTA and PFSA Reduction
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Solution Overview
Problem
Existing session log analysis methods are inefficient in producing concise and easily understandable reports for large-scale web applications, as they struggle with complex user interactions and cross-session relationships, leading to time-consuming and expensive reporting processes.
Innovation Solution
A visual reporting system utilizing Probabilistic Finite-State Automatons (PFAs) and Prefix Tree Automatons (PTAs) to process and reduce user session logs, enabling analysts to generate interactive and clickable maps of user session data, allowing for the visualization of complex patterns and transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional session log analysis methods are used to process large-scale user interaction data, then comprehensive user behavioral information can be obtained, but the reporting process becomes time-consuming and expensive
Solution Approach 1:
The patent segments the complex session log analysis process into distinct computational stages: (1) parsing user session logs into discrete events, (2) constructing Prefix Tree Automata (PTA) to represent user navigation patterns, and (3) reducing PTAs to Probabilistic Finite-State Automatons (PFSA) for efficient analysis. This segmentation allows each stage to be optimized independently and processed in parallel, dramatically reducing overall reporting time while preserving complete user behavioral information.
Solution Approach 2:
The patent replaces traditional mechanical report generation methods with automated computational systems. Specifically, it uses algorithmic construction of PTAs and PFSA to automatically extract user behavioral patterns from session logs, eliminating manual analysis processes. The system substitutes human-driven report creation with machine-learning-based automaton reduction techniques, achieving both speed and comprehensive information retention.
2Measurement precision
If detailed user session data is analyzed to understand complex user interactions, then accurate user behavioral insights are achieved, but the complexity of processing and visualizing the data increases
Solution Approach 1:
The patent extracts essential user behavioral patterns from complex session logs by identifying and isolating key navigation events. The PTA construction process extracts only the relevant state transitions and event sequences needed for analysis, filtering out redundant information. The subsequent PFSA reduction further extracts the core probabilistic patterns, maintaining measurement precision while simplifying the data structure for easier processing and visualization.
Solution Approach 2:
The patent transforms the representation of user session data by changing parameters from raw event logs to structured automaton models. The transition from PTA to PFSA involves parameter changes in state representation, transition probabilities, and event sequencing. This parameter transformation maintains the precision of user behavioral measurement while converting complex unstructured data into a standardized format that is more manageable and visualizable.
3Loss of information
If comprehensive user session logs are processed to capture all user activities, then complete behavioral patterns are obtained, but the data volume and processing cost increase
Solution Approach 1:
The patent merges multiple user session logs into a unified PTA model that represents aggregate user behavior patterns. By combining individual session data into a single automaton structure, the system captures comprehensive behavioral patterns across all users while avoiding the need to process and store each individual session separately. The PFSA reduction further merges similar state transitions and consolidates probabilistic patterns, significantly reducing data volume while preserving complete behavioral insights.
Data Source
AI summary
In various exemplary embodiments, a system and method is disclosed to analyze event streams of a user session. One embodiment of the method includes logging a plurality of events from an event stream, specifying conditions to sort the plurality of events, applying the conditions to the plurality of events, producing a probability distribution of event nodes after a given node in the event stream, and producing an interactive graphical reduction of a produced graph by determining patterns in a graphical tree and unifying occurrences of the patterns.