Handling incomplete data using semantic logging based social network analysis hexagon for effective application monitoring and management
Bibliographic record
Abstract
Monitoring and management of large scale applications is already a complex task because of syntactic and unstructured nature of execution data. Traditional application monitoring and management solutions focused on employing analysis techniques on unstructured and syntactic log information become limited as unstructured information cannot be well utilized to find out related events information or correlate such information with other related information from applications. Our proposed solution of semantically formalized logging fills this gap by bringing formal semantics and combining it in a meaningful way to enable automated monitoring and management of applications. Such formalized and well-structured log information helps analytical solution to maximally automate the process of monitoring and management of applications. However, while formalizing and structuring the log information, we came across several missing and incomplete data which causes hindrance in this process. In this paper, we tackle this problem and propose a social network analysis based solution to handle incomplete and missing data from application execution, possibly compute it and use it by our proposed solution of semantically formalizing and structured logs with adapted data mining techniques to enable automated and effective application monitoring and management. We demonstrate from an industrial use-case application that how historical data from application execution is stored using semantic logging and utilized with standard social-network analysis techniques to find out missing values in incomplete data and perform application monitoring and management.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".