Condition-Triggered Data Classification for Tables and Columns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data platforms face inefficiencies in automatic data classification due to the need for manual intervention, resource-intensive custom pipelines, and delays in classifying large datasets, leading to increased operational costs, vulnerabilities, and non-compliance with data protection regulations.
Innovation Solution
Implementing an automatic classification profile with conditions for triggering data classification, which intelligently classifies data in columns and tables based on predefined rules, reducing manual effort and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data classification is used, then classification accuracy can be maintained, but operational costs increase and productivity decreases
Solution Approach 1:
The system enables self-service by allowing the data classification process to automatically analyze and categorize data without requiring manual intervention. The classification engine autonomously processes data, applies predefined rules, and assigns categories, thereby eliminating the need for human operators while maintaining classification accuracy and significantly improving productivity.
Solution Approach 2:
The patent replaces the mechanical manual classification process with an automated computational system. The classification engine uses algorithms and predefined rules to automatically determine data categories, substituting human cognitive processing with machine-based decision-making,ไป่ achieving both high productivity and full automation.
2Adaptability or versatility
If custom pipelines are implemented for data classification, then classification capability can be enhanced, but device complexity and operational costs increase
Solution Approach 1:
The system implements universality by providing a single, unified classification engine that can handle multiple classification tasks and data types through a common framework. The engine applies predefined rules and patterns that can be universally applied across different datasets, eliminating the need for separate custom pipelines for each classification scenario while maintaining flexibility and adaptability.
Solution Approach 2:
The patent utilizes parameter changes by allowing the classification engine to adapt to different data characteristics and classification requirements through configurable parameters and rules. Rather than requiring complex custom pipelines, the system adjusts its behavior by modifying classification parameters and rule sets, achieving flexibility without increasing overall system complexity.
3Quantity of substance
If large datasets are classified manually, then classification completeness can be achieved, but time consumption and resource usage increase
Solution Approach 1:
The patent replaces manual data processing with an automated classification engine that can rapidly analyze and categorize large datasets. The system uses computational algorithms to process data at scale, eliminating the time-consuming manual review process while maintaining completeness. The engine can handle massive volumes of data simultaneously, achieving both high quantity processing and minimal time loss.
Solution Approach 2:
The system implements continuous classification processing, where the engine operates without interruption to analyze and categorize data as it is generated or uploaded. This continuous operation ensures that large datasets are processed completely and efficiently, eliminating the time loss associated with batch processing or manual review cycles.
4Productivity
If automatic classification is implemented, then productivity increases, but measurement precision and detection accuracy may decrease
Solution Approach 1:
The system incorporates feedback mechanisms that allow the classification engine to learn from and adapt to patterns in the data. The engine continuously refines its classification decisions by analyzing feedback from the data structure and content, ensuring high accuracy while maintaining high throughput. This feedback loop enables the system to achieve both productivity and measurement precision.
Solution Approach 2:
The patent replaces manual classification with an automated engine that uses sophisticated algorithms and pattern recognition to maintain high accuracy. The system substitutes human judgment with machine-based analysis that can process data at scale while preserving precision through rule-based decision-making and pattern matching, thereby achieving both high productivity and measurement precision.
Data Source
AI summary
Systems and methods are provided for classifying data. The systems and methods access an automatic classification profile comprising one or more conditions for triggering data classification and access a classification scope that identifies one or more tables to be classified. The systems and methods determine that a set of attributes of the one or more tables identified by the classification scope corresponds to the one or more conditions of the automatic classification profile. The systems and methods, in response to determining that the set of attributes of the one or more tables identified by the classification scope corresponds to the one or more conditions of the automatic classification profile, automatically classify data stored in one or more columns of the one or more tables.