How muhammadyahta/manufacturingproductionlinedatasetgeneration-football- works
This repository combines a synthetic football-production data generator with a separate ontology-based RDF mapping application. The main user workflow is to choose a generation range, create and aggregate production-line records, and write an Excel result; the documented KG workflow maps production data to ontology terms and supports querying the resulting dataset. The Python entry-point orchestration and parts of the Java mapper are visible; omitted Java code leaves its exact input and output wiring uncertain.
Main entry points: run.py Generator CLI · ImportCSVFile.java CSV RDF mapper · utils.py Process generation · queries Example queries
Components 15
Data generation
- Generator CLI
dgPython/run.py - Production sample
dgPython/DataSampleUpdated.xlsx - Generator config
dgPython/config.json - Process generation
dgPython/utils.py - Line process stages
dgPython/utils.py - Record aggregation
dgPython/utils.py - Generated records
dgPython/result.xlsx
Semantic mapping
- CSV RDF mapper
rdmap/com.nuig/src/main/java/CSVtoRDFFile/com/nuig/ImportCSVFile.java - Production ontology
rdmap/com.nuig/model/smO.owl - Production knowledge graph
Dataset access
- Virtuoso server
- Example queries
queries - KG embedding evaluation
Other components
- Dataset user
- Dataset analysts
Connections 13
- Dataset user → Generator CLI: runs
- Generator CLI → Production sample: reads
- Generator CLI → Generator config: loads
- Generator CLI → Process generation: calls
- Process generation → Line process stages: generates
- Generator CLI → Record aggregation: calls
- Generator CLI → Generated records: writes
- CSV RDF mapper → Production ontology: loads
- CSV RDF mapper → Production knowledge graph: populates
- Production knowledge graph → Virtuoso server: uploads to
- Dataset analysts → Virtuoso server: query
- Example queries → Virtuoso server: queries
- Virtuoso server → KG embedding evaluation: provides data