UniPart: Optimizing Streaming Graph Partitioning Towards Universal Adaption in RDF Triple Stores
Bibliographic record
Abstract
Purpose: With increasing size of Resource Description Framework (RDF) graphs, the resulting graph structures can become too large to be managed on a single compute node, lacking the necessary resources to execute a partitioning of the graph – in particular, when the partitioning method relies on global graph information for which the entire graph has to be loaded into the main memory. This paper introduces a window-based streaming partitioning technique to obtain distributed RDF graphs, overcoming the memory limitations of traditional partitioning methods. Methodology: We evaluated our approach, UniPart, by comparing it with established graph partitioning algorithms such as METIS, LDG, and WStream. The comparison focused on key metrics, including the proportion of edge cuts. Findings: Through practical assessments using the LUBM dataset, our algorithm demonstrated strong performance in load balance, execution time, and memory usage. Notably, under the DFS streaming order, UniPart achieved a 20% reduction in edge-cut ratio compared to LDG. Value: UniPart operates without the need for global graph information, making it exceptionally suited for dynamic environments with unbounded streams and unpredictable data sizes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".