TGDB: towards a benchmark for graph databases
Bibliographic record
Abstract
Graph data has become an important representation for many analytical applications, ranging from social network analysis to biological data computation, to ontologies in the semantic web. Recently, many graph databases have been proposed to process and analyze graph data. We can categorize these into two main approaches: one is to build a layer of graph data model on top of an existing database (e.g., key-value store); and the second is to build a specialized native data processing substrate for processing graph data. Consequently, data scientists at present have a variety of choices and approaches to choose amongst. This requires having an approach to evaluate and assess these approaches, to select the one that suits best their situation. We propose TGDB, the Toronto Graph Database Benchmark. TGDB has query workload and real-world datasets to evaluate the performance of targeted systems. We choose three graph databases that have different system architectures and evaluate their performance against TGDB.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".