Caching techniques for XML message filtering
Bibliographic record
Abstract
An XML publish/subscribe system is based on filtering XML message streams for a large number of subscriptions expressed in XPath. A major issue on an XML-based publish/subscribe system is its performance. As the number of XML documents and XPath-based subscriptions increases in the system, to provide XML filtering efficiently becomes a challenging problem. Hence, there is an urgent need for optimization techniques to meet this challenge. There are many existing approaches on designing efficient XML filtering engine. Most existing research efforts focus on efficient filtering algorithms for achieving a high system performance or supporting more complex XPath syntax. Each proposed scheme has its advantages and limitations. Not much research, however, has considered using caching in the context of XML filtering. In this paper, we propose two caching schemes to be used in conjunction with an XML filtering engine. First, we present a complete message caching algorithm that is a strict caching policy to reduce the computation cost that accrues from multiple filtering of the same messages, by reusing results of previously processed messages. Second, we investigate a structure-based caching method that is an approximate caching policy for messages sharing the same structure. Performance evaluation for synthetic data and real data both show that complete message caching and structure-based caching schemes are able to achieve significantly better filtering performance (up to 80% for both caching schemes for the message streams experimented with).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".