The OLAP-Enabled Grid: Model and Query Processing Algorithms
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.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".