Update-pattern-aware modeling and processing of continuous queries
Bibliographic record
Abstract
A defining characteristic of continuous queries over on-line data streams, possibly bounded by sliding windows, is the potentially infinite and time-evolving nature of their inputs and outputs. New items continually arrive on the input streams and new results are continually produced. Additionally, inputs expire by falling out of range of their sliding windows and results expire when they cease to satisfy the query. This impacts continuous query processing in two ways. First, data stream systems allow tables to be queried alongside data streams, but in terms of query semantics, it is not clear how updates of tables are different from insertions and deletions caused by the movement of the sliding windows. Second, many interesting queries need to store state, which must be kept up-to-date as time goes on. Therefore, query processing efficiency depends highly on the amount of overhead involved in state maintenance.In this paper, we show that the above issues can be solved by understanding the update patterns of continuous queries and exploiting them during query processing. We propose a classification that defines four types of update characteristics. Using our classification, we present a definition of continuous query semantics that clearly states the role of relations. We then propose the notion of update-pattern-aware query processing, where physical implementations of query operators, including the data structures used for storing intermediate state, vary depending on the update patterns of their inputs and outputs. When tested on IP traffic logs, our update-pattern-aware query plans routinely outperform the existing techniques by an order of magnitude.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".