Semantic-Driven Model Composition for Accurate Anomaly Diagnosis
Bibliographic record
Abstract
In this paper, we introduce a semantic-driven approach to system modeling for improving the accuracy of anomaly diagnosis. Our framework composes heterogeneous families of models, including generic statistical models, and resource-specific models into a belief network, i.e., Bayesian network. Given a set of models which sense the behavior of various system components, the key idea is to incorporate expert knowledge about the system structure and dependencies within this structure, as meta-correlations across components and models. Our approach is flexible, easily extensible and does not put undue burden on the system administrator. Expert beliefs about the system hierarchy, relationships and known problems can guide learning, but do not need to be fully specified. The system dynamically evolves its beliefs about anomalies over time. We evaluate our prototype implementation on a dynamic content site running the TPC-W industry-standard e- commerce benchmark. We sketch a system structure and train our belief network using automatic fault injection. We demonstrate that our technique provides accurate problem diagnosis in cases of single and multiple faults. We also show that our semantic-driven modeling approach effectively finds the component containing the root cause of injected anomalies, and avoids false alarms for normal changes in environment or workload.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".