A Flexible Data-Driven Approach for Execution Trace Filtering
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
Execution traces are frequently used to study system run-time behavior and to detect problems. However, the huge amount of data in an execution trace may complexify its analysis. Moreover, users are not usually interested in all events of a trace, hence the need for a proper filtering approach. Filtering is used to generate an enhanced trace, with a reduced size and complexity, that is easier to analyse. The approach described in this paper allows to define custom filtering patterns, declaratively in XML, to concentrate the analysis on the most important and interesting events. The filtering scenarios include syntaxes to describe various analysis patterns using finite state machines. The patterns range from very simple event filtering to complex multi-level event abstraction, covering various types of synthetic behaviours that can be captured from execution trace data. The paper provides the details on this data-driven filtering approach and some interesting use cases for the trace events generated by the LTTng Linux kernel tracer.
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 | 0.001 | 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 | 0.001 | 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