Cube data model for multilevel statistics computation of live execution traces
Bibliographic record
Abstract
Summary Execution trace logs are used to analyze system run‐time behaviour and detect problems. Trace analysis tools usually read the input logs and gather either a detailed or brief summary of them to later process and inspect in the analysis steps. However, continuous and lengthy trace streams contained in the live tracing mode make it difficult to indefinitely record all events or even a detailed summary of the whole stream. This situation is further complicated when the system aims to compare different parts of the trace and provide a multilevel and multidimensional analysis. This paper presents an architecture with corresponding data structures and algorithms to process stream events, generate an adequate summary—detailed enough for recent data and succinct enough for old data—and organize them to enable an efficient multilevel and multidimensional analysis, similar to online analytical processing analyses in the database applications. The proposed solution arranges data in a compact manner using interval forms and enables the range queries for any arbitrary time durations. Because this feature makes it possible to compare of different system parameters in different time areas, it significantly influences the system's ability to provide a comprehensive trace analysis. Although the Linux operating system trace logs are used to evaluate the solution, we propose a generic architecture that can be used to summarize various types of stream data. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".