NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering (replication package)
Bibliographic record
Abstract
This is the replication package for the paper "NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering" where we present Neo Modeling Framework (NMF), an open-source set of tools primarily designed to manipulate ultra-large datasets in the Neo4j database. Repository structure NeoModelingFramework.zip - contains the replication package, including the source code for NMF, test files to run the evaluation, used artifacts, and instructions to run the framework. The most import folders are listed below: codeGenerator - NMF generator module modelLoader - NMF loader module modelEditor - NMF editor module Evaluation - contains the evaluation artifacts and results (a copy metamodels - Ecore files used for RQ1 and RQ2 results - CSV files with the results from RQ1, RQ2 and RQ3 analysis - Jupyter notebooks used to analyze and plot the results Running NMF The best way to run NMF is following the instructions at our GitHub repository. A copy of the Readme file is also present inside the zip file available here. Empirical Evaluation Make sure that you follow the instructions to run NMF. The quantitative evaluation can be re-run by running RQ1Eval.kt, RQ2Eval.kt inside modelLoader/src/test/kotlin/evaluation and RQ2Eval.kt inside modelEditor/src/test/kotlin/evaluation. Make sure that you have an empty instance of Neo4j running. Results will be generated as CSV files, under Evaluation/results and the results can be plotted by running the Jupyter Notebooks at Evaluation/analysis. Please note that due to differences in hardware, re-running the experiments will probably generate slightly different results than those reported in the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".