DAMOCRO: A Data Migration Framework Using Online Classification and Reordering
Bibliographic record
Abstract
This paper introduces DAMOCRO, a data migration framework using online classification and tuple reordering 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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".