Event Type Prediction for Mobile App SDK Logging
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Solution Overview
Problem
Online systems face challenges in logging information about events from mobile applications consistently due to varying descriptions of the same events by different application developers, leading to inconsistencies that hinder interpretation and analysis.
Innovation Solution
An online system predicts the type of event occurring in a mobile application using machine-learning models or sets of rules, based on received information, and logs the predicted type, ensuring consistent logging by mapping keywords to event types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different application developers use their own descriptions for the same types of events, then the mobile applications can be developed with flexibility and creativity, but the online system cannot log events in a consistent manner
Solution Approach 1:
The patent introduces an intermediary layer (event type determination module) that translates varied developer descriptions into standardized event types. This mediator receives diverse event descriptions from different applications and maps them to consistent event types using machine learning models, thereby maintaining both developer flexibility and system consistency.
Solution Approach 2:
The system changes the parameter representation of events by transforming free-text descriptions into standardized event type categories. Machine learning models analyze the semantic content of descriptions and map them to predefined event types, effectively changing the parameter space from unstructured text to structured classifications.
2Productivity
If the online system logs events using the exact descriptions provided by developers, then the logging process is simple and direct, but the logged information becomes difficult to interpret and analyze
Solution Approach 1:
The system performs preliminary action by determining event types before final logging. The event type determination module processes descriptions and assigns standardized types in advance, so that when events are logged, they already carry interpretable classification information without sacrificing logging speed.
Solution Approach 2:
The patent segments the event logging process into distinct stages: receiving the event description, determining the event type through machine learning analysis, and then logging the structured information. This segmentation allows the system to maintain high productivity while ensuring interpretability through intermediate processing.
3Measurement precision
If the online system implements a comprehensive analysis of event descriptions using machine learning, then the event type determination accuracy improves, but the system complexity increases
Solution Approach 1:
The system employs self-service by using machine learning models that automatically analyze event descriptions and determine event types without requiring manual configuration or intervention. The models are trained on historical data and autonomously classify new events, improving accuracy while managing complexity through automation.
Solution Approach 2:
The patent implements a universal event type determination module that handles diverse event descriptions from multiple applications using a single machine learning framework. This multi-functional approach improves determination accuracy across different event types while avoiding the complexity of maintaining separate processing systems for each application.
Data Source
AI summary
An online system receives information including a description of an event occurring in a mobile application and user identifying information associated with a user of the mobile application associated with the event via an SDK incorporated into the mobile application code of the mobile application. The online system determines whether the description corresponds to information maintained in the online system describing types of events capable of occurring in the mobile application. If the description corresponds to information describing a type of event capable of occurring in the mobile application, the online system logs the type of event in association with the user identifying information. Otherwise, the online system predicts a type of event corresponding to the event occurring in the mobile application based at least in part on the information received at the online system and logs the predicted type of event in association with the user identifying information.