Dynamic Classifier Priority Ordering for Large-Scale Data Discovery
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Solution Overview
Problem
Conventional systems inefficiently process large datasets, consuming significant resources and exposing sensitive data to security risks due to the use of a single, predefined order of classifiers, leading to high latency and potential data breaches.
Innovation Solution
The classification priority management system dynamically updates the priority order of classifiers based on match rates within a dataset, prioritizing relevant classifiers for efficient and secure data classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems process large datasets using a single predefined order of classifiers, then the classification process can be implemented with simple logic, but it consumes significant computing resources and takes several days to complete
Solution Approach 1:
The patent applies dynamics by transitioning from a static, predefined classifier order to a dynamic priority ordering system. The system automatically adjusts the order of classifiers based on real-time match rates and data characteristics, allowing the classification process to adapt and optimize its speed without sacrificing accuracy. This dynamic reordering enables faster processing by prioritizing classifiers that yield the most effective results.
Solution Approach 2:
The system changes the parameter of classifier priority ordering from fixed to variable. By monitoring match rates and adjusting the priority sequence of classifiers dynamically, the system optimizes processing efficiency. This parameter change allows the system to respond to different data patterns and achieve faster classification speeds while maintaining simplicity in implementation.
2Device complexity
If conventional systems process large amounts of data sequentially without considering data types, then the processing logic remains simple, but it exposes sensitive data to security risks and increases latency
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors match rates between classifiers and data elements. This feedback loop enables the system to adjust classifier priorities in real-time, ensuring that sensitive data is quickly identified and classified. The feedback-driven optimization enhances security and reduces latency while maintaining relatively simple processing logic through automated adjustments.
Solution Approach 2:
The classification system performs self-service by automatically determining optimal classifier priorities based on observed data patterns and match rates. Rather than requiring complex external control, the system self-adjusts its processing sequence to enhance security and reduce latency. This self-optimizing capability maintains simplicity in the overall processing logic while achieving improved reliability.
3Ease of manufacture
If conventional systems use fixed classification methods, then the implementation is straightforward, but it fails to meet time-sensitive requirements for real-time fraud detection
Solution Approach 1:
The system transforms the fixed classification method into a dynamic one by continuously adjusting classifier priorities based on real-time match rates. This dynamic adaptation enables the system to meet time-sensitive requirements for fraud detection while maintaining ease of implementation through automated optimization. The dynamic priority reordering reduces classification time without requiring complex manual configuration.
Solution Approach 2:
The system achieves self-service optimization by automatically determining the best classifier sequence based on observed data patterns. This self-adjusting mechanism reduces classification time for real-time fraud detection while keeping the implementation straightforward. The system self-optimizes its performance without requiring complex external intervention, maintaining ease of manufacture while eliminating time losses.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for updating the priority of classifiers in a classifier model. Specifically, the disclosed systems execute operations to extract data elements from a digital dataset. The disclosed system generates first classifier labels for a first subset of data elements (e.g., a test dataset) by utilizing a classification model to apply a predetermined order of classifiers to the first subset of data elements. The disclosed systems utilize the first classifier labels to determine a priority order for the classifiers for applying to a second subset of data elements the digital dataset. Using the determined priority order of the classifiers, the disclosed systems can generate second classifier labels for a second subset of data elements by utilizing the classifier model to apply the classifiers according to the priority order.