Towards a uniform data model in multi-database environments
Bibliographic record
Abstract
The massive amounts of heterogeneous data available today give rise to numerous opportunities for new services. At same time, new complex challenges are implied by volume, velocity and variability of data at hand. A fundamental conceptual shift facing software architects today is obsolescence of what used to be called the data tier, which, in pursuit of performance, has now been fragmented into federations of relational and NoSQL repositories with denormalized and duplicated data. In this paper, we review some of relevant research and technological background, discuss challenges associated with a multi-database environment, and put forward a research road-map for creating a common data model to support inter-database consistency, workflow automation, and data management.
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 | 0.052 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.030 | 0.049 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".