LogView: Visualizing Event Log Clusters
Bibliographic record
Abstract
Event logs or log files form an essential part of any network management and administration setup. While log files are invaluable to a network administrator, the vast amount of data they sometimes contain can be overwhelming and can sometimes hinder rather than facilitate the tasks of a network administrator. For this reason several event clustering algorithms for log files have been proposed, one of which is the event clustering algorithm proposed by Risto Vaarandi, on which his simple log file clustering tool (SLCT) is based. The aim of this work is to develop a visualization tool that can be used to view log files based on the clusters produced by SLCT. The proposed visualization tool, which is called LogView, utilizes treemaps to visualize the hierarchical structure of the clusters produced by SLCT. Our results based on different application log files show that LogView can ease the summarization of vast amount of data contained in the log files. This in turn can help to speed up the analysis of event data in order to detect any security issues on a given application.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".