Automatic Recovery of Database Structure (ARDS)
Bibliographic record
Abstract
One of the evolutions of information technology which is a very fascinating feature is the application of the auto recovery. This feature enables an external system to automatically diagnose other systems, detects the error that causes the failure, then recovers and reconfigures the system. The concept of software and web auto recovery is widely used in much software such as windows operating system which restores and recovers tools.  Since the aim is to fast recover the application and keep it running and available as optimal as possible then it will be suitable to apply this capability to the database applications to fast recover from any unexpected change that may happen. This paper proposes an auto-recovery system that monitors, diagnoses, checks and heals database applications automatically and immediately with unnoticeable recovery time. The aim is to recover and to redo the changes that happened to the database by internal unauthorized user or external intrusion. To test the practical applicability of the proposed methodology, an application has been developed to demonstrate the methodology and apply it for real time database applications. The results of experiments performed on different scenarios demonstrated the ability of the proposed framework to recover database applications.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".