This repository comprises the data for the experimental studies. This includes the scripts to generate queries under set and utility-aware semantics for the queries from FedBench. Furthermore, the quality descriptions as well as the resulting queries from the experimental study are provided as well.
The scripts are written in Python 3.
The scripts require the packages numpy and pandas.
Furthermore, as the queries are executed using the ARQ CLI , the binary must be available as well and set correctly when running the execute_query.py script.
The query preparation step creates the (optimized) SPARQL 1.1 queries for the query decompositions generated by FedX.
The script can be run using Python. Note that the ARQ CLI must be installed to run the script.
Create a SPARQL 1.1 under set semantics query from a decomposition:
python3 prepare_query.py -d decompositions/type1/CD1_decomp.txt -q quality_descriptions/1_qm/description_qm_1_v_1.jsonCreate a SPARQL 1.1 under utility-aware semantics query from a decomposition:
python3 prepare_query.py -o -d decompositions/type1/CD1_decomp.txt -q quality_descriptions/1_qm/description_qm_1_v_1.jsonThe option -v can be used to obtain more metadata about the query.
Get Help:
python3 prepare_query.py --helpThe query execution execute_query.py script is a simple interface to run queries using the ARQ CLI.
It writes the results to a CSV File and also stores statistics about the execution.
The script can be run using Python. Note that the ARQ CLI must be installed to run the script.
Execute a query:
python3 execute_query.py -q queries/examples/example_query.rq -a ~/apache-jena-3.6.0/bin/arqGet Help:
python3 execute_query.py --help