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
The overwhelming acceptance of the SQL standard \cite{Date94} 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 integrated view produced. The complexities of querying across database systems and resolving conflicts are too numerous to be fully described here, so this paper will discuss querying the integrated view of a single database. The integration architecture integrates database schema information into a context view (CV). The context view is a high-level view of database semantics which allows logically and physically transparent access to the underlying data source(s). Since this context view is an entirely new way of organizing and categorizing database information, a new query language is developed. However, we demonstrate that the context view has similar properties as the Universal Relational Model and thus can benefit from its associated algorithms and ideas. By allowing the user to query by context and semantic connotation, a whole new level of query complexity arises. Mapping of queries from semantic concepts to physical tables, fields, and relationships must be automatically performed. We will demonstrate that specific relational calculus expressions or SQL queries can be generated from abstract concepts which are rigorous enough for use in industrial applications and systems. Specifically, SQL generation and join discovery are overviewed. Thus, the query language can be mapped to SQL allowing backwards compatibility with existing systems. Notes: Joint released technical report. Released as TR-00-16 for the University of Manitoba, and 2000-663-15 for the University of Calgary.
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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