Using semantic information to improve transparent query caching for dynamic content Web sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it