TAPER: query-aware, partition-enhancement for large, heterogenous,\n graphs
Bibliographic record
Abstract
Graph partitioning has long been seen as a viable approach to address Graph\nDBMS scalability. A partitioning, however, may introduce extra query processing\nlatency unless it is sensitive to a specific query workload, and optimised to\nminimise inter-partition traversals for that workload. Additionally, it should\nalso be possible to incrementally adjust the partitioning in reaction to\nchanges in the graph topology, the query workload, or both. Because of their\ncomplexity, current partitioning algorithms fall short of one or both of these\nrequirements, as they are designed for offline use and as one-off operations.\nThe TAPER system aims to address both requirements, whilst leveraging existing\npartitioning algorithms. TAPER takes any given initial partitioning as a\nstarting point, and iteratively adjusts it by swapping chosen vertices across\npartitions, heuristically reducing the probability of inter-partition\ntraversals for a given pattern matching queries workload. Iterations are\ninexpensive thanks to time and space optimisations in the underlying support\ndata structures. We evaluate TAPER on two different large test graphs and over\nrealistic query workloads. Our results indicate that, given a hash-based\npartitioning, TAPER reduces the number of inter-partition traversals by around\n80%; given an unweighted METIS partitioning, by around 30%. These reductions\nare achieved within 8 iterations and with the additional advantage of being\nworkload-aware and usable online.\n
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".