User Action String Pattern Recognition via Sorted Location Indexing
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Solution Overview
Problem
Existing analytical tools for user journey analytics are static and inefficient, limited in recognizing complex patterns within large sequences of user actions, leading to high computational burdens and inability to dynamically process user data for efficient search techniques.
Innovation Solution
The techniques involve breaking down action strings into sorted lists of locations for particular actions, enabling the use of binary search to quickly identify subsequences, thereby converting complex data into a highly searchable format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static analytical tools are used to track user actions, then implementation is simple, but pattern recognition capability is limited
Solution Approach 1:
The system transitions from static analytical tools to dynamic pattern recognition by continuously adapting to different user action sequences. The algorithm dynamically identifies patterns in real-time user journeys, allowing the system to handle diverse and evolving user behaviors rather than being confined to pre-defined static patterns.
Solution Approach 2:
The system changes its operational parameters by adjusting pattern matching criteria based on the specific user journey being analyzed. Rather than using fixed parameters, the system adapts its search and analysis parameters dynamically to optimize pattern recognition for different types of user interactions and journey lengths.
2Measurement precision
If complex sequences of user actions are analyzed in detail, then analysis precision is improved, but computational burden increases
Solution Approach 1:
The system segments the complex user action sequences into smaller, manageable components or events. By breaking down long user journeys into discrete action events, the system can analyze each segment independently and efficiently, reducing the overall computational burden while maintaining precise pattern recognition across the complete journey.
Solution Approach 2:
The system extracts only the relevant and significant actions from complex user sequences, filtering out redundant or less important events. This extraction process identifies key pattern-defining actions while discarding unnecessary data, thereby maintaining high analysis precision with reduced computational requirements.
3Speed
If traditional search techniques are used on action strings, then implementation is straightforward, but processing speed is slow
Solution Approach 1:
The system performs preliminary processing of user action data by pre-segmenting and indexing action sequences before actual pattern searching occurs. This preliminary organization of data into structured formats enables much faster retrieval and pattern matching operations, significantly improving processing speed while the added structural complexity is managed through efficient data structures.
Data Source
AI summary
Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.