Optimizing In-Order Execution of Continuous Queries over Streamed Sensor Data
Bibliographic record
Abstract
The widespread use of sensor networks in scientific and engineering applications leads to increased demand on the efficient computation of the collected sensor data. Recent research in sensor and stream data systems adopts the notion of sliding windows to process continuous queries over infinite sensor readings. Ordered processing of input data is essential during query execution for many application scenarios. In this paper we present three approaches for ordered execution of continuous sliding window queries over sensor data. The first approach enforces ordered processing at the input side of the query execution plan. In the second approach we utilize the advantage of out-of-order execution to optimize query operators and enforce an ordered release of the output results. The third approach is adaptive and switches between the first and second approaches to achieve the best overall performance with current input arrival rates and level of multiprogramming. We study the performance of the proposed approaches both analytically and experimentally and under a variety of conditions such as the asynchronous arrival of input data, and various levels of multiprogramming. Our performance study is based on an extensive set of experiments using a realization of the proposed approaches in a prototype stream query processing system.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".