Seven Commandments for Benchmarking Semantic Flow Processing Systems

Over the last few years, the processing of dynamic data has gained increasing attention in the Semantic Web community. This led to the development of several stream reasoning systems that enable on-the-fly processing of semantically annotated data that changes over time. Due to their streaming nature, analyzing such systems is extremely difficult. Currently, their evaluation is conducted under heterogeneous scenarios, which makes it hard to clearly compare them, understanding the benefits and limitations of each of them. In this paper, we strive for a better understanding the key challenges that these systems must face and define a generic methodology to evaluate their performance. Specifically, we identify three Key Performance Indicators (KPIs) and seven commandments that specify how to design the stress tests for system evaluation. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-78691 Accepted Version Originally published at: Scharrenbach, Thomas; Urbani, Jacopo; Margara, Alessandro; Della Valle, Emanuele; Bernstein, Abraham (2013). Seven commandments for benchmarking semantic flow processing systems. In: The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings, Montpellier, 26 May 2013 30 May 2013, 305-319. Seven Commandments for Benchmarking Semantic Flow Processing Systems Thomas Scharrenbach, Jacopo Urbani, Alessandro Margara, Emanuele Della Valle, Abraham Bernstein 1 University of Zurich [email protected] 2 Vrije Universiteit Amsterdam [email protected], [email protected] 3 Politecnico di Milano [email protected] Abstract. Over the last few years, the processing of dynamic data has Over the last few years, the processing of dynamic data has gained increasing attention in the Semantic Web community. This led to the development of several stream reasoning systems that enable on-thefly processing of semantically annotated data that changes over time. Due to their streaming nature, analyzing such systems is extremely difficult. Currently, their evaluation is conducted under heterogeneous scenarios, which makes it hard to clearly compare them, understanding the benefits and limitations of each of them. In this paper, we strive for a better understanding the key challenges that these systems must face and define a generic methodology to evaluate their performance. Specifically, we identify three Key Performance Indicators (KPIs) and seven commandments that specify how to design the stress tests for system evaluation.