Time-Windowed Counters for Event Tracking in Social Networks
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Solution Overview
Problem
Social networking systems face challenges in tracking and classifying events and assets effectively as the volume and variety of user interactions increase, leading to difficulties in optimizing features and functionality based on event types and frequencies.
Innovation Solution
Implementing time-windowed counters to generate time series of event occurrences, determining signal values, and classifying assets using decision trees or machine learning models to assess uniqueness and trends, allowing for real-time policy modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional event tracking methods are used to monitor user interactions in social networks, then basic event counting is possible, but the system cannot effectively handle the exponentially increasing volume and variety of events as the network grows
Solution Approach 1:
The patent segments event tracking into multiple dimensions by introducing time windows and event types. Instead of tracking all events uniformly, the system divides events into different categories (e.g., click, like, share) and time periods, allowing manageable analysis of large event volumes through structured segmentation of the event space.
2Loss of information
If comprehensive event tracking is implemented to capture all user interactions, then detailed insights into user behavior are achieved, but the complexity of processing and analyzing the data increases significantly
Solution Approach 1:
The patent applies local quality by treating different event types and time windows with different tracking priorities. Not all events are tracked with the same granularity - the system adjusts the level of detail based on the specific event type and its relevance to business objectives, reducing processing complexity while maintaining information completeness for critical events.
Solution Approach 2:
The system changes parameters by introducing time window sizes and event type classifications as additional dimensions. By varying these parameters, the system can adjust the granularity of tracking to balance information completeness with processing complexity, allowing flexible adaptation to different analytical needs.
3Loss of time
If real-time event analysis is performed to identify trends and classify assets, then timely policy modifications are enabled, but the computational resources and processing time required increase
Solution Approach 1:
The patent implements preliminary action by pre-defining event types, time windows, and classification rules before events occur. This preparation work is done offline, so when events are tracked in real-time, the system only needs to match incoming events against pre-established criteria, significantly reducing computational resources required for real-time analysis while maintaining fast response times.
4Measurement precision
If detailed classification of assets based on event patterns is implemented, then more accurate policy decisions are made, but the complexity of the classification system and models increases
Solution Approach 1:
The patent segments the classification problem by breaking it down into distinct event types and time window combinations. Instead of creating a single complex classification model for all events, the system creates simpler models for specific event types and time periods, reducing overall system complexity while maintaining high classification accuracy through targeted segmentation.
Data Source
AI summary
Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.