Using semantic information to improve transparent query caching for dynamic content Web sites
Bibliographic record
Abstract
In this paper, we study the use of semantic information to improve performance of transparent query caching for dynamic content Web sites. We observe that in dynamic content Web applications, the most recently inserted items are also the ones that register the highest activity. For example, the newest books in a bookstore are also the ones more frequently browsed and bought. Hence, assuming repeatable queries, a particular read-only query response is likely to incrementally change as new rows are added to the queries tables. We avoid the cached query response invalidations that would otherwise occur due to the addition of new items by keeping the newly inserted rows in small temporary tables. This allows us to reuse cached responses for partial coverage of query results. A query result is then obtained from merging an existing cached response with one or more lightweight residual query results that involve the temporary tables. In addition, we enhance our cache with other partial coverage techniques based on per-query semantic information such as sub-range queries for all queries that match a specific template. We implement semantic query caching on top of an existing template-based cache with column-based invalidations. Our evaluation is based on a dynamic content site using the Apache Web server with Tomcat Java servlets and the MySQL relational database. We use the industry-standard TPC-W e-commerce benchmark as our benchmark application. We conclude that augmenting transparent query caching with the ability to retrieve partial results from the cache improves performance substantially in terms of latency and to a lesser extent in terms of hit-rate and throughput.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".