Answering tree pattern queries using views
Bibliographic record
Abstract
We study the query answering using views (QAV) problem for tree pattern queries. Given a query and a view, the QAV problem is traditionally formulated in two ways: (i) find an equivalent rewriting of the query using only the view, or (ii) find a maximal contained rewriting using only the view. The former is appropriate for classical query optimization and was recently studied by Xu and Ozsoyoglu for tree pattern queries (TP). However, for information integration, we cannot rely on equivalent rewriting and must instead use maximal contained rewriting as shown by Halevy. Motivated by this, we study maximal contained rewriting for TP, a core subset of XPath, both in the absence and presence of a schema. In the absence of a schema, we show there are queries whose maximal contained rewriting (MCR) can only be expressed as the union of exponentially many TPs. We characterize the existence of a maximal contained rewriting and give a polynomial time algorithm for testing the existence of an MCR. We also give an algorithm for generating the MCR when one exists. We then consider QAV in the presence of a schema. We characterize the existence of a maximal contained rewriting when the schema contains no recursion or union types, and show that it consists of at most one TP. We give an efficient polynomial time algorithm for generating the maximal contained rewriting whenever it exists. Finally, we discuss QAV in the presence of recursive schemas.
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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 | 0.013 |
| 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 | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".