Machine Data Source Type Detection Using Punctuation Pattern ML
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Solution Overview
Problem
The challenge of accurately determining the source type of ingested data in large and diverse IT environments, which is crucial for effective data analysis, is exacerbated by the growing volume of minimally processed machine data, leading to inefficiencies in data retrieval and analysis.
Innovation Solution
A computerized method for analyzing ingested data using punctuation patterns to determine the source type, enabling accurate labeling and correcting mislabeling through machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are applied to predict source type, then source type labeling accuracy is improved, but processing time and computational resources are increased
Solution Approach 1:
The system performs preliminary actions by extracting punctuation patterns from the machine data before applying machine learning classification. This pre-processing step prepares the data in advance, allowing the ML model to work with simplified features and reducing overall processing time while maintaining high accuracy in source type prediction.
Solution Approach 2:
The invention extracts punctuation patterns from the machine data as a separate feature set. By taking out this specific characteristic (punctuation patterns) from the raw data, the system creates a simplified representation that can be efficiently processed by machine learning algorithms, reducing computational complexity while preserving essential information for accurate source type identification.
2Measurement precision
If machine learning models are trained to detect mislabeling, then detection accuracy is improved, but model complexity and training requirements are increased
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously learns from labeled data and corrects mislabeling detected during operation. This feedback loop allows the model to improve its accuracy over time without requiring complex retraining procedures, as the system adapts to new patterns and corrects errors dynamically.
Solution Approach 2:
The invention uses copying by creating a simplified representation of the data space focused on punctuation patterns. Instead of analyzing the entire complex machine data structure, the system copies only the relevant punctuation features, reducing model complexity while maintaining effective mislabeling detection capability.
3Measurement precision
If punctuation patterns are extracted and analyzed, then source type identification accuracy is improved, but data processing complexity is increased
Solution Approach 1:
The system extracts punctuation patterns from the machine data as a separate, manageable feature set. By taking out only the punctuation elements and analyzing them in isolation, the system reduces the complexity of data processing while maintaining high accuracy in source type identification. This extraction creates a simplified representation that is easier to process computationally.
Solution Approach 2:
The invention segments the data processing task by separating punctuation pattern extraction from the main data analysis. This segmentation allows the system to handle punctuation analysis as a distinct, optimized process, reducing overall processing complexity while improving source type identification accuracy through focused analysis of the extracted patterns.
Data Source
AI summary
Implementations of the disclosure pertain to detecting mislabeling of a source type assigned to machine data through utilization of machine learning techniques. Operations of a computerized method for detecting the mislabeling include receiving machine data that has been assigned an initial source type upon receipt by a data intake and query system and parsing the data block into a plurality of events based on a source type definition of the initial source type. Further operations include generating a data representation of a first event being a portion of the machine data and is associated with a point in time, determining a predicted source type of the first event by at least analyzing the data representation through machine learning techniques, and performing a comparison between the predicted source type and the initial source type thereby determining whether the source type of the event was initially mislabeled.