LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping
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
Large-scale software systems generate vast volumes of system logs that are essential for monitoring, diagnosing, and performance optimization. However, the unstructured nature and ever-growing scale of these logs present significant challenges for manual analysis and automated downstream tasks such as anomaly detection. Log parsing addresses these challenges by converting raw logs into structured formats, enabling efficient log analysis. Despite its importance, existing log parsing methods suffer from limitations in efficiency and scalability, due to the large size of log data and their heterogeneous formats. To overcome these challenges, this study proposes a log parsing approach, LogLSHD, which leverages Locality-Sensitive Hashing (LSH) to group similar logs and integrates Dynamic Time Warping (DTW) to enhance the accuracy of template extraction. LogLSHD demonstrates exceptional efficiency in parsing time, significantly outperforming state-of-the-art methods. For example, compared to Drain, LogLSHD reduces the average parsing time by 73% while increasing the average parsing accuracy by 15% on the LogHub 2.0 benchmark.
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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 | 0.001 |
| Open science | 0.000 | 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