An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions
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
AIOps (Artificial Intelligence for IT Operations) leverages machine learning models to help practitioners handle the massive data produced during the operations of large-scale systems. However, due to the nature of the operation data, AIOps modeling faces several data splitting-related challenges, such as imbalanced data, data leakage, and concept drift. In this work, we study the data leakage and concept drift challenges in the context of AIOps and evaluate the impact of different modeling decisions on such challenges. Specifically, we perform a case study on two commonly studied AIOps applications: (1) predicting job failures based on trace data from a large-scale cluster environment and (2) predicting disk failures based on disk monitoring data from a large-scale cloud storage environment. First, we observe that the data leakage issue exists in AIOps solutions. Using a time-based splitting of training and validation datasets can significantly reduce such data leakage, making it more appropriate than using a random splitting in the AIOps context. Second, we show that AIOps solutions suffer from concept drift. Periodically updating AIOps models can help mitigate the impact of such concept drift, while the performance benefit and the modeling cost of increasing the update frequency depend largely on the application data and the used models. Our findings encourage future studies and practices on developing AIOps solutions to pay attention to their data-splitting decisions to handle the data leakage and concept drift challenges.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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