Window query processing for joining data streams with relations
Bibliographic record
Abstract
Query processing for data streams raises challenges that cannot be directly handled by existing database management systems (DBMS). Most related work in the literature mainly focuses on developing techniques for a dedicated data stream management system (DSMS). These systems typically either do not permit joining data streams with conventional relations or simply convert relations to streams before joining. In this paper, we present techniques to process queries that join data streams with relations, without treating relations as special streams. We focus on a typical type of such queries, called star-streaming joins. We process these queries based on the semantics of (sliding) window joins over data streams and apply a load shedding approximation when system resources are limited. A recently proposed window join approximation based on importance semantics for data streams is extended in this paper to maximize the total importance of the approximation result of a star-streaming join. Both online and offline approximation algorithms are discussed. Our experimental results demonstrate that the presented techniques are quite promising in processing star-streaming joins to achieve the maximum total importance of their approximation results.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".