An access cost-aware approach for object retrieval over multiple sources
Bibliographic record
Abstract
Source and object selection and retrieval from large multi-source data sets are fundamental operations in many applications. In this paper, we initiate research on efficient source (e.g., database) and object selection algorithms on large multi-source data sets. Specifically, in order to acquire a specified number of satisfying objects with minimum cost over multiple databases, the query engine needs to determine the access overhead for individual data sources, the overhead of retrieving objects from each source, and possibly other statistics such as estimating the frequency of finding a satisfying object in order to determine how many objects to retrieve from each data source. We adopt a probabilistic approach to source selection utilizing a cost structure and a dynamic programming model for computing the optimal number of objects to retrieve from each data source. Such a structure can be a valuable asset where there is a monetary or time related cost associated with accessing large distributed databases. We present a thorough experimental evaluation to validate our techniques using real-world data sets.
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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 | 0.001 | 0.002 |
| Open science | 0.004 | 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".