A query answering system for data with evolution relationships
Bibliographic record
Abstract
Evolving data has attracted considerable research attention. Researchers have focused on modeling and querying of schema/instance-level structural changes, such as, insertion, deletion and modification of attributes. Databases with such a functionality are known as temporal databases. A limitation of the temporal databases is that they treat changes as independent events, while often the appearance (or elimination) of some structure in the database is the result of an evolution of some existing structure. We claim that maintaining the causal relationship between the two structures is of major importance since it allows additional reasoning to be performed and answers to be generated for queries that previously had no answers. We present the TrenDS, a system for exploiting the evolution relationships between the structures in the database. In particular, our system combines different structures that are associated through evolution relationships into virtual structures to be used during query answering. The virtual structures define ``possible'' database instances, in a fashion similar to the possible worlds in the probabilistic databases. TrenDS uses a query answering mechanism that allows queries to be answered over these possible databases without materializing them. Evaluation of such queries raises many technical challenges, since it requires the discovery of Steiner forests on the evolution graphs.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".