A Lightweight Algorithm for Message Type Extraction in System Application Logs
Bibliographic record
Abstract
Message type or message cluster extraction is an important task in the analysis of system logs in computer networks. Defining these message types automatically facilitates the automatic analysis of system logs. When the message types that exist in a log file are represented explicitly, they can form the basis for carrying out other automatic application log analysis tasks. In this paper, we introduce a novel algorithm for carrying out message type extraction from event log files. IPLoM, which stands for Iterative Partitioning Log Mining, works through a 4-step process. The first three steps hierarchically partition the event log into groups of event log messages or event clusters. In its fourth and final stage, IPLoM produces a message type description or line format for each of the message clusters. IPLoM is able to find clusters in data irrespective of the frequency of its instances in the data, it scales gracefully in the case of long message type patterns and produces message type descriptions at a level of abstraction, which is preferred by a human observer. Evaluations show that IPLoM outperforms similar algorithms statistically significantly.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".