Mining unstructured log files for recurrent fault diagnosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enterprise software systems are large and complex with limited support for automated root-cause analysis. Avoiding system downtime and loss of revenue dictates a fast and efficient root-cause analysis process. Operator practice and academic research have shown that about 80% of failures in such systems have recurrent causes; therefore, significant efficiency gains can be achieved by automating their identification. In this paper, we present a novel approach to modelling features of log files. This model offers a compact representation of log data that can be efficiently extracted from large amounts of monitoring data. We also use decision-tree classifiers to learn and classify symptoms of recurrent faults. This representation enables automated fault matching and, in addition, enables human investigators to understand manifestations of failure easily. Our model does not require any access to application source code, a specification of log messages, or deep application knowledge. We evaluate our proposal using fault-injection experiments against other proposals in the field. First, we show that the features needed for symptom definition can be extracted more efficiently than does related work. Second, we show that these features enable an accurate classification of recurrent faults using only standard machine learning techniques. This enables us to identify accurately up to 78% of the faults in our evaluation data set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it