Faster Querying for Database Integration and Virtualization with Distributed Semi-Joins
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
Data integration and virtualization is commonly used to combine data for data analytics and reporting. A major challenge is handling large data sizes (“Big Data”) as moving data across a network is extremely expensive and limits query processing. Business intelligence and data visualization software require rapid response times for users, and data virtualization is often limited for use cases involving joins across systems. The contribution of this work is a semi-join based approach to data virtualization joins that minimizes data movement and utilizes the extensive resources available in the database systems rather than performing query processing in the virtualization engine. The result is significantly less data movement which translates into faster query times and higher performance. Experimental results demonstrate that performance can be increased by an order of magnitude. A unique feature of the approach is that it does not require any special software installed above the database servers such as mediators and works directly using SQL queries.
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 | 0.002 |
| 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