Self-learning log classification using text and image classifiers
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Solution Overview
Problem
Existing data archival systems face challenges with inaccurate data classification, inefficient storage, and unorganized record keeping, particularly as large volumes of electronic data accumulate, making it difficult to meaningfully identify and classify data in a timely manner.
Innovation Solution
A self-learning log classification system that uses machine-learning categorization methods, including text classifiers, multiclass classifiers, and combination classifiers, to accurately categorize log entries by identifying datafields and metafields, and applying weighted metrics to determine categorical associations, while continuously improving classification accuracy through feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data archival systems are used to store large quantities of electronic data, then storage capacity is maintained, but data classification accuracy deteriorates and organization efficiency decreases
Solution Approach 1:
The system implements feedback mechanisms where classification results are continuously evaluated and used to refine classification rules. The system learns from past classification outcomes, adjusting its categorization logic to improve accuracy over time while maintaining efficient processing of large data volumes.
Solution Approach 2:
The classification system performs self-learning and self-optimization without requiring manual intervention. It automatically adjusts classification parameters and rules based on accumulated data patterns, enabling both high accuracy and sustained productivity in data organization.
2Measurement precision
If manual classification methods are used for electronic data, then classification detail can be maintained, but processing time increases significantly
Solution Approach 1:
The system replaces manual mechanical classification processes with automated machine learning algorithms. These algorithms analyze data patterns and apply classification rules automatically, achieving both high precision in categorization and rapid processing speeds that eliminate the time bottleneck of manual methods.
Solution Approach 2:
The system introduces an intelligent intermediary layer between raw data and final storage. This layer automatically performs classification analysis using learned patterns, providing accurate categorization without requiring direct human intervention, thus maintaining precision while dramatically reducing processing time.
3Measurement precision
If classification rules are made more detailed to improve accuracy, then categorization precision improves, but system complexity increases
Solution Approach 1:
The classification system employs dynamic rules that adapt based on data characteristics and context. Rather than static complex rules, the system adjusts classification criteria dynamically, simplifying the decision process for common cases while maintaining detailed analysis only when necessary, thus achieving high accuracy without proportional increases in system complexity.
Solution Approach 2:
The system segments the classification process into multiple hierarchical levels. Simple classifications are handled by basic rules, while more complex categorizations are processed by specialized sub-routines. This modular approach maintains high overall accuracy without requiring the entire system to be uniformly complex.
Data Source
AI summary
A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.