The OLAP-Enabled Grid: Model and Query Processing Algorithms
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 operation of modern distributed enterprises, be they commercial, scientific, or health related, generate massive quantities of data. Decision makers increasingly utilize On- Line Analytical Processing (OLAP) tools to glean from this rich data resource nuggets of information which can be used to better run their enterprises. A typical approach to OLAP is to construct a single centralized data repository by copying all of the raw data from the sites where it is generated to a cental location, where it is integrated, and then to route all queries to that central location. As the amount of data and number of sites and users grows this approach suffers from significant scalability problems. In this paper, we present a model and algorithmic framework for an "OLAP-Enabled Grid" whose goal is the efficient support of OLAP operations. We show how a Grid computing infrastructure can be used to store and manage expensive to compute data aggregations and to answer OLAP queries in a fully distributed manner. Our focus is on the efficient optimization of resources for answering queries based on a distributed query algorithm which uses cached and pre-aggregated data stored over a Grid computing infrastructure.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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