Multi‐scale navigation of large trace data: A survey
Bibliographic record
Abstract
Summary Dynamic analysis through execution traces is frequently used to analyze the runtime behavior of software systems. However, tracing long running executions generates voluminous data, which are complicated to analyze and manage. Extracting interesting performance or correctness characteristics out of large traces of data from several processes and threads is a challenging task. Trace abstraction and visualization are potential solutions to alleviate this challenge. Several efforts have been made over the years in many subfields of computer science for trace data collection, maintenance, analysis, and visualization. Many analyses start with an inspection of an overview of the trace, before digging deeper and studying more focused and detailed data. These techniques are common and well supported in geographical information systems, automatically adjusting the level of details depending on the scale. However, most trace visualization tools operate at a single level of representation, which are not adequate to support multilevel analysis. Sophisticated techniques and heuristics are needed to address this problem. Multi‐scale (multilevel) visualization with support for zoom and focus operations is an effective way to enable this kind of analysis. Considerable research and several surveys are proposed in the literature in the field of trace visualization. However, multi‐scale visualization has yet received little attention. In this paper, we provide a survey and methodological structure for categorizing tools and techniques aiming at multi‐scale abstraction and visualization of execution trace data and discuss the requirements and challenges faced to be able to meet evolving user demands.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".