Query optimization in multidatabase systems
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
A multidatabase system (MDBS) integrates information from autonomous local databases managed by heterogeneous database management systems (DBMS) in a distributed environment. For a query involving more than one database, global query optimization should be performed to achieve good overall system performance. The significant differences between an MDBS and a traditional distributed database system (DDBS) make query optimization in the former more challenging than in the latter. Challenges for query optimization in an MDBS are discussed in this paper. A two-phase optimization approach for processing a query in an MDBS is proposed. Several global query optimization techniques suitable for an MDBS, such as semantic query optimization, query optimization via probing queries, parametric query optimization and adaptive query optimization, are suggested. The architecture of a global query optimizer incorporating these techniques is designed.
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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 | 0.000 | 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 it