Adaptive Execution of Stream Window Joins in a Limited Memory Environment
Bibliographic record
Abstract
A sliding window join (SWJoin) is becoming an integral operation in every stream data management system. In some streaming applications the increasing volume of streamed data as well as the multiplicity of concurrent queries requires an adaptive SWJoin algorithm for the limited memory resources. Previous algorithms of SWJoin address the memory limitation by exploiting external-memory resources while imposing timely ordered arrival of input data streams. In this paper we propose an external-memory sliding-window join algorithm (EM-SWJoin) that addresses general arrival patterns of input streams and exploits disk- based data structures. The algorithm runs in two phases. The first phase partially joins the arriving data of one stream with the memory-resident data of the other streams. The second phase completes the processing of the partially joined data by considering the disk-resident data from the corresponding streams. Swapping from one phase to the other improves the response time of the input data. A comparative study between EM-SWJoin and other related algorithms illustrates the superiority of the proposed algorithm.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".