GTFS-Madrid-Bench

doi:doi:10.57702/ldr8qrii

GTFS-Madrid-Bench

A benchmark to evaluate declarative KG construction engines that can be used to provide access mechanisms to (virtual) knowledge graphs. Our proposal introduces several scenarios that aim at measuring performance and scalability as well as the query capabilities of all these kinds of engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They can be scaled up into several formats (CSV, JSON, SQL, and XML). The query set addresses a representative number of SPARQL 1.1 features while covering the usual queries in which data consumers may be interested.

BibTex:

@dataset{David_Chaves-Fraga_and_Freddy_Priyatna_and_Jhon_Toledo_and_Daniel_Doña_and_Edna_Ruckhaus_and_Andrea_Cimmino_and_Oscar_Corcho_2023,
    abstract = {A benchmark to evaluate declarative KG construction engines that can be used to provide access mechanisms to (virtual) knowledge graphs. Our proposal introduces several scenarios that aim at measuring performance and scalability as well as the query capabilities of all these kinds of engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They can be scaled up into several formats (CSV, JSON, SQL, and XML). The query set addresses a representative number of SPARQL 1.1 features while covering the usual queries in which data consumers may be interested.},
    author = {David Chaves-Fraga and Freddy Priyatna and Jhon Toledo and Daniel Doña and Edna Ruckhaus and Andrea Cimmino and Oscar Corcho},
    doi = {10.57702/ldr8qrii},
    institution = {Universidad Politécnica de Madrid},
    keyword = {'Benchmark'},
    month = {oct},
    publisher = {TIB},
    title = {GTFS-Madrid-Bench},
    url = {https://service.tib.eu/ldmservice/dataset/gtfs-madrid-bench},
    year = {2023}
}