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Satisfiability of Façade-X BGP

Implementation of two algorithms for Façade-X BGP satisfiability.

Top down / Search: AnalyserAsSearch (inefficient)

Bottom up / CSP: AnalyserGrounder (efficient)

We compare the two algorithms with designed queries, see EXPERIMENTS.md for an example of output.

We then perform experiments with real world queries collected from GitHub public repositories, and with queries of the GTFS benchmark. With those, we only perform experiments with the Bottom up/CSP algorithm.

See REAL-WORLD-QUERIES.md for an example of output.

See GTFS-QUERIES.md for an example of output.

Build and run experiments

Building or running tests with maven will also re-generate the file EXPERIMENTS.md

mvn clean install

or

mvn test

Run the experiments 10 times

Use the script runAll.sh to generate 10 execution files EXPERIMENTS-run-N.md/.csv, REAL-WORLD-QUERIES-run-N.md/.csv, and GTFS-QUERIES-run-N.md/.csv

The script was tested only on a MacBook Pro environment.

Build tables

We use SPARQL Anything to aggregate the data from the 10 executions, fx stands for java -jar -Xmx=4g sparql-anything-v1.0.0.jar:

fx -q experiments-tables.sparql -v algorithm=T -v complete=false -o "TopDown_Satisfiability.csv"
fx -q experiments-tables.sparql -v algorithm=T -v complete=true -o "TopDown_SolutionPatterns.csv"
fx -q experiments-tables.sparql -v algorithm=B -v complete=false -o "BottomUp_Satisfiability.csv"
fx -q experiments-tables.sparql -v algorithm=B -v complete=true -o "BottomUp_SolutionPatterns.csv"
fx -q rwq-experiments-tables.sparql -v complete=false -o "RWQ_Satisfiability.csv"
fx -q rwq-experiments-tables.sparql -v complete=true -o "RWQ_SolutionPatterns.csv"
fx -q gtfs-experiments-tables.sparql -v complete=false -o "GTFS_Satisfiability.csv"
fx -q gtfs-experiments-tables.sparql -v complete=true -o "GTFS_SolutionPatterns.csv"

Real world queries

We collected queries mentioning the sparql anything service clause on GitHub. The process and data is documented in this repository.

Number of queries analysed: 449 (rwq-number-of-queries.sparql)

Number of BGP identified per queries:

query_n,number_of_bgp

queries number of bgp
228 1
79 2
37 3
32 4
22 5
11 11
9 10
6 7
4 6
4 9
2 13
2 22
2 26
2 27
1 12
1 17
1 20
1 24
1 25
1 29
1 31
1 40
1 8

Data analysis

See this google spreadsheet for further analysis