Managing massive graphs in relational DBMS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Massive graphs emerge in many real-world applications. Practitioners often find relational databases are inefficient in graph data management. In this paper, we investigate the efficiency issue by analyzing both I/O and CPU costs. First, we find the storage of a graph in relational DBMS violates the locality principle: graph queries will always reference neighbors; however, the data locations of neighbors are almost random. To solve this problem, we introduce partitioned graph storage as a new database design option. It combines database partitioning with available graph-partitioning algorithms to restructure the storage such that neighbors are located close to each other. Second, we find graph queries expressed with SQL introduce unnecessary overheads. To overcome the CPU costs, we propose a new storage access method, which we call graph scan, to retrieve neighbors in one single operation. We show experimentally that partitioned graph storage and graph scan can significantly reduce I/O and CPU costs. We conclude that a relational DBMS could be a good graph store, as long as the storage respects the locality principle and SQL overheads are eliminated.
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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 | 0.000 | 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 | 0.001 |
| 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 it