A Graph-based Data Mining Approach for Template Recognition using Large Log Datasets in Software Systems
Bibliographic record
Abstract
Log files generated by software systems can be utilized as a valuable resource in data-driven approaches to improve the system health and stability. These files often contain valuable information about runtime execution, and their effective monitoring requires analyzing an increasingly large volume of data logs. In this paper, a graph mining technique for log parsing is presented, which is source agnostic to the system. This means that the technique can function regardless of the source of the logs, making it more scalable and reusable. Unlike the existing approaches that rely heavily on domain knowledge and regular expression patterns, the proposed approach uses graph models and semantic analysis to detect data patterns with minimal user input. This makes it easy to implement it in a variety of scenarios where application-based logs may differ significantly. The proposed parsing technique is evaluated over seven datasets. It achieves the best performance on the Thunderbird dataset, where the technique takes 3.87 seconds for 2000 logs, while obtaining precision, recall and F1 measure higher than 0.99.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".