Event Data Analysis via Specificity Scoring
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Solution Overview
Problem
Analyzing event data that includes filter interactions is computationally expensive and inefficient, particularly when dealing with large volumes of data and complex patterns.
Innovation Solution
An event data analysis system that identifies user interactions with data filters, generates specificity scores to measure filter specificity, filters event data based on these scores, and analyzes filtered data to identify pivot points and relevant filter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional event data analysis methods are used to process large volumes of event data with filter interactions, then comprehensive analysis coverage is achieved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments event data into different types (clickstream, transaction, sensor data) and processes each segment with appropriate analysis methods. It also segments the analysis into phases: initial filtering using specificity scores, intermediate processing of filtered data, and final pattern identification, thereby reducing overall computational burden while maintaining comprehensive analysis coverage
Solution Approach 2:
The patent extracts and removes low-value or redundant event data through filtering mechanisms before detailed analysis. By extracting only relevant event data based on predefined criteria and specificity scores, the system eliminates unnecessary computational processing of irrelevant data while preserving comprehensive analysis of meaningful events
2Measurement precision
If detailed analysis of all event data is performed to identify relevant patterns, then detection accuracy improves, but computing resources are excessively consumed
Solution Approach 1:
The patent performs preliminary filtering of event data using specificity scores and relevance criteria before conducting detailed pattern analysis. This preliminary action identifies and retains only high-value events that are likely to contain relevant patterns, thereby maintaining detection accuracy while significantly reducing the volume of data requiring computationally intensive analysis
Solution Approach 2:
The patent applies different analysis depths and processing qualities to different segments of event data based on their relevance and importance. High-specificity events receive detailed analysis with high computational resources, while low-specificity events receive minimal processing or are discarded, optimizing the distribution of computing resources across the entire dataset
3Measurement precision
If complex filtering and analysis operations are applied to event data streams, then relevant filter values are identified more accurately, but processing time increases
Solution Approach 1:
The patent implements periodic sampling and batch processing of event data streams, applying complex filtering and analysis operations at regular intervals rather than continuously. This periodic approach maintains accurate identification of relevant filter values while reducing overall processing time by allowing intervals of lighter computational activity between analysis cycles
Data Source
AI summary
In some implementations, an event data analysis system may identify event data that indicates user interactions with an application, each of the user interactions corresponding to an interaction with data filters. The system may generate specificity scores, each of the specificity scores indicating, for a corresponding user interaction, a measure of specificity associated with data filters that correspond to the corresponding user interaction. The system may filter the event data stream based on the specificity scores. The system may generate at least one entropy value for at least one data filter, each entropy value indicating a rate of change of a corresponding data filter. The system may identify, based on the specificity scores and the at least one entropy value, a set of relevant filter values associated with the event data. The system may provide output indicating the set of relevant filter values.