Log Message Classification Using Random Forest for Search Efficiency
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Solution Overview
Problem
Conventional approaches are time-consuming and resource-intensive when identifying relevant log entries in voluminous log files, often relying on brute-force text searching which is computationally expensive and may not yield accurate or sufficiently narrow results.
Innovation Solution
A log message classification system employing machine learning, specifically using a random forest algorithm, to classify incoming log messages, thereby invoking the appropriate parser for interpreting fields and values, and periodically retraining the model to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute-force text searching is used to identify relevant log entries, then comprehensive search coverage is achieved, but search time and computational resources increase substantially
Solution Approach 1:
The system performs preliminary classification of log messages using machine learning models before actual search operations. Log messages are pre-tagged with categories, parsers, and confidence scores, so when a search is initiated, the system can quickly filter and retrieve relevant entries without scanning the entire log file. This preliminary organization dramatically reduces search time while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary classification layer between the raw log messages and the search function. This intermediary system uses trained machine learning models to generate structured metadata (parsing rules, confidence scores, category tags) that act as intermediaries to bridge the gap between unstructured log data and structured search queries, enabling efficient and accurate retrieval.
2Productivity
If machine learning classification is implemented to parse log messages, then search efficiency improves, but system complexity increases
Solution Approach 1:
The patent employs universal machine learning models (such as random forest classifiers) that can handle multiple types of log message formats simultaneously. Instead of creating separate parsing systems for each log format, a single trained model can classify and parse various log types, reducing overall system complexity while maintaining high search efficiency across diverse log sources.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters such as confidence score thresholds and model selection based on the specific search context. Rather than maintaining a fixed complex architecture, the system adapts its classification parameters and model complexity to match the requirements of each search task, optimizing the balance between efficiency and complexity.
3Reliability
If log messages are stored in original text format, then data integrity is preserved, but query and reporting performance deteriorates
Solution Approach 1:
The patent segments log messages into their constituent fields and values using parser-based classification. Each log message is broken down into structured components (timestamp, source, event type, parameters, etc.) while the original text is preserved. This segmentation enables efficient querying of specific fields without compromising the integrity of the original log data, as both the structured breakdown and original text coexist.
Solution Approach 2:
The system creates structured copies of log messages with extracted fields and values while maintaining the original text format. These copied structured representations are used for indexing, querying, and reporting operations, whereas the original unstructured logs remain intact for audit and verification purposes. This copying approach enables high-performance queries without sacrificing data integrity.
Data Source
AI summary
A log message classifier employs machine learning for identifying a corresponding parser for interpreting the incoming log message and for retraining a classification logic model processing the incoming log messages. Voluminous log messages generate a large amount of data, typically in a text form. Data fields are parseable from the message by a parser that knows a format of the message. The classification logic is trained by a set of messages having a known format for defining groups of messages recognizable by a corresponding parser. The classification logic is defined by a random forest that outputs a corresponding group and confidence value for each incoming message. Groups may be split to define new groups based on a recurring matching tail (latter portion) of the incoming messages. A trend of decreased confidence scores triggers a periodic retraining of the random forest, and may also generate an alert to operators.