μBE: User Guided Source Selection and Schema Mediation for Internet Scale Data Integration
Bibliographic record
Abstract
The typical approach to data integration is to start by defining a common mediated schema, and then to map the data sources being integrated to this schema. In Internet-scale data integration tasks, where there may be hundreds or thousands of data sources providing data of relevance to a particular domain, a better approach is to allow the user to discover the mediated schema and the set of sources to use through an iterative exploration of the space of possible schemas and sources. In this paper, we present μBE, a data integration tool that helps in this iterative exploratory process by automatically choosing the data sources to include in a data integration system and defining a mediated schema on these sources. The data integration system desired by the user may depend on several subjective and objective criteria, and the user guides μBE towards finding this system by iteratively solving a series of constrained non-linear optimization problems, and modifying the parameters and constraints of the problem in the next iteration based on the solution found in the current iteration. Our formulation of the optimization problem is designed to make it easy for the user to provide such feedback. A simple, intuitive user interface helps the user in this process. We experimentally demonstrate that μBE is efficient and finds high-quality data integration solutions.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".