Handling sharable queries in both streaming and stored XML documents
Bibliographic record
Abstract
XML Publish/Subscribe systems are system-active and user-passive systems that asynchronously serve users’ queries. These queries are usually stored and later executed when matching XML documents become available. With large amounts of subscriptions and in the presence of sharable queries, efficient execution of these queries becomes a substantial requirement. In this work, we describe a novel approach that uses new physical algebra which is capable of handling a group of sharable queries that are submitted against streaming and/or stored XML documents. Our grouping approach is designed to share computations and eliminate redundant executions by producing a single algebraic query execution plan (QEP) for all resident queries and sharing execution in composed physical operators. To assess the effectiveness of our approach, we designed and implemented the physical algebra operators in a system which we then used to conduct experiments that helped us to compare between processing a single query and that of group of queries. Details of our proposed approach, the design and implementation of the related system and the results of our experimental study are presented in this article, together with a discussion of planned directions of future work.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.011 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".