ORCA: a Benchmark for Data Web Crawlers

• Main Authors: Röder, Michael, de Souza, Geraldo, Kuchelev, Denis, Desouki, Abdelmoneim Amer, Ngomo, Axel-Cyrille Ngonga
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2019
• Subjects: Databases cs.DB; Performance cs.PF; FOS Computer and information sciences; Orca
• Online Access: https://dx.doi.org/10.48550/arxiv.1912.08026; https://arxiv.org/abs/1912.08026

The number of RDF knowledge graphs available on the Web grows constantly. Gathering these graphs at large scale for downstream applications hence requires the use of crawlers. Although Data Web crawlers exist, and general Web crawlers could be adapted to focus on the Data Web, there is currently no benchmark to fairly evaluate their performance. Our work closes this gap by presenting the Orca benchmark. Orca generates a synthetic Data Web, which is decoupled from the original Web and enables a fair and repeatable comparison of Data Web crawlers. Our evaluations show that Orca can be used to reveal the different advantages and disadvantages of existing crawlers. The benchmark is open-source and available at https://github.com/dice-group/orca. : 8 pages, submitted to a conference