DAMOCRO: A Data Migration Framework Using Online Classification and Reordering
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
This paper introduces DAMOCRO, a <u>da</u>ta <u>m</u>igration framework using <u>o</u>nline <u>c</u>lassification and tuple <u>r</u>e<u>o</u>rdering to improve throughput and decrease the costs of data migration. The DAMOCRO workflow consists of four main steps. First, it classifies records into subgroups to maximize the similarity within each group. Next, it reorders tuples within these groups, ensuring that similar tuples are adjacent. Subsequently, column-wise compression is applied to each group. Finally, the compressed data is transferred from the source to the target machine. The initial two steps enhance the compression ratio, thereby boosting throughput and reducing costs. Our evaluations on five real-world datasets and two benchmark datasets, show that the online classification process in DAMOCRO improves throughput by more than 24% and reduces costs by over 19% compared to baselines. Besides, implementing reordering based on functional dependencies brings an additional cost reduction ranging from 10% to 60%, while also enhancing throughput.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it