Multidatabase Querying by Context 1 University of Calgary 2000-663-15 University of Manitoba TR-00-16
Bibliographic record
Abstract
The overwhelming acceptance of the SQL standard [10] has curtailed continuing research work in relational database query languages and processing.Since all commercial relational database systems conform with the SQL standard, there is little motivation for developing new query languages. Despite its benefits and wide-spread acceptance, SQL is not a perfect query language.Complex database schema challenge even experienced database users during query formulation.As increasing numbers of less sophisticated users access numerous data sources within an organization or across the Internet, their ability to accurately construct queries with the appropriate structure and semantics diminishes.SQL can be hard to use as it provides only physical access transparency not logical transparency. That is, a user is responsible for mapping the semantics of their query to the semantics and structure of the database.Although graphical tools for query construction and high-level programming languages mask some of the complexity, the notion of querying by structure is intrinsic to most forms of data access. In this work, we overview a new query language developed in conjunction with our integration architecture for automatically integrating relational schema.Although the major focus of this work is on database interoperability, the contribution of this paper is a language for specifying queries on the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".