SCHEMA MEDIATION IN PEER DATA MANAGEMENT SYSTEMS
Bibliographic record
Abstract
Peer Data Management Systems (PDMSs) allow the efficient sharing of data between peers with overlapping sources of information. These sources share data through mappings between peers. In current systems, queries are asked over each peer's local schema and then translated using the mappings between peers. While this allows the data to be accessed uniformly, users lack access to information that is not in their own schemas. In this paper, we propose a light-weight, automatic method to create a mediated schema in a PDMS. Our work benefits PDMSs by allowing access to more data and without unduly stressing the peer's resources or requiring additional resources such as ontologies. We present our system — MePSys, which creates a mediated schema in PDMSs automatically using the existing mappings provided to translate queries. We further discuss how to update the mediated schema in a stable state, i.e. after the system setup period.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.002 | 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".