Towards intensional answers to OLAP queries for analytical sessions
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
One of the problems in analyzing large multidimensional databases through OLAP sessions is that decision makers can be overwhelmed by the size of query answers, while they need a concise summary of data. Intensional query answering can help by providing a concise description of extensional answers (i.e., the sets of retrieved facts), generally relying on knowledge like integrity constraints, taxonomies, or patterns discovered from data. This paper proposes a framework for computing an intensional answer to an OLAP query by leveraging on the previous queries in the current session. Such intensional answer is concise and semantically rich, and allows the size of the extensional answers returned to be reduced, so as to achieve an effective trade-off between conciseness and informational content. After describing the general framework, we propose a specific instantiation that relies on previous contributions in cube modeling and intensional query answering.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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