A Unified Narrative for Query Processing in Graph Databases
Bibliographic record
Abstract
With the advent of graph data, graph databases have garnered significant research interest and efforts in recent years, especially with respect to graph query processing. There have been a vast suite of methods for efficient graph query processing, especially for the core graph query constructs, regular path queries (RPQs) and subgraph matching queries (SMQs). In the meantime, there is an observable divide among these methods as well as confusion between them and their relational counterparts. We thus propose this tutorial to provide a unified narrative for graph query processing, so as to bridge the gap between existent lines of work and offer a comprehensive view of the query processing workflow in graph databases.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".