TGDB: towards a benchmark for graph databases
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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