Processing Unions of Conjunctive Queries with Negation under Limited Access Patterns
Bibliographic record
Abstract
Abstract. We study the problem of finding executable query plans over distributed sources with limited access patterns. This problem is becoming increasingly important in the area of distributed query processing, most notably, web services. For the purposes of query planning, web services can be seen as remote procedure calls with input/output access pattern restrictions. The problem is to decide whether a given query Q is feasible, i.e., whether one can find an equivalent executable query Q ′ that observes the limited access patterns given by the sources. We characterize the complexity of deciding feasibility for the classes CQ ¬ (conjunctive queries with negation) and UCQ ¬ (unions of CQ ¬ queries), which has been open until now: testing feasibility is just as hard as testing containment and therefore Π P 2-complete. We also provide a uniform treatment for CQ, UCQ, CQ ¬ , and UCQ ¬ by devising a single algorithm which is optimal for each of these classes. In addition, we show how one can often avoid the worst-case complexity by certain approximations at compile-time and at runtime. At compile-time, even if a query Q is not feasible, we can find efficiently the minimal executable query containing Q. For query answering at runtime, we devise an algorithm which may report complete answers even in the case of infeasible plans and which can indicate to the user the degree of completeness for certain incomplete answers. 1
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".