XML Index Recommendation with Tight Optimizer Coupling
Bibliographic record
Abstract
XML database systems are expected to handle increasingly complex queries over increasingly large and highly structured XML databases. An important problem that needs to be solved for these systems is how to choose the best set of indexes for a given workload. In this paper, we present an XML Index Advisor that solves this XML index recommendation problem and has the key characteristic of being tightly coupled with the query optimizer. We rely on the optimizer to enumerate index candidates and to estimate the benefit gained from potential index configurations. We expand the set of candidate indexes obtained from the query optimizer to include more general indexes that can be useful for queries other than those in the training workload. To recommend an index configuration, we introduce two new search algorithms. The first algorithm finds the best set of indexes for the specific training workload, and the second algorithm finds a general set of indexes that can benefit the training workload as well as other similar workloads. We have implemented our XML Index Advisor in a prototype version of IBMregDB2reg9, which supports both relational and XML data, and we experimentally demonstrate the effectiveness of our advisor using this implementation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".