Trying Not to Die Benchmarking: Orchestrating RDF and Graph Data Management Solution Benchmarks Using LITMUS

Knowledge graphs, usually modelled via RDF or property graphs, have gained importance over the past decade. In order to decide which Data Management Solution (DMS) performs best for specific query loads over a knowledge graph, it is required to perform benchmarks. Benchmarking is an extremely tedious task demanding repetitive manual effort, therefore it is advantageous to automate the whole process. However, there is currently no benchmarking framework which supports benchmarking and comparing diverse DMSs for both RDF and property graph DMS. To this end, we introduce, the first working prototype of, LITMUS which provides this functionality as well as fine-grained environment configuration options, a comprehensive set of DMS and CPU-specific key performance indicators and a quick analytical support via custom visualization (i.e. plots) for the benchmarked DMSs.

[1]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[2]  Julian Dolby,et al.  Building an efficient RDF store over a relational database , 2013, SIGMOD '13.

[3]  Brian W. Barrett,et al.  Introducing the Graph 500 , 2010 .

[4]  Marko A. Rodriguez,et al.  The Gremlin graph traversal machine and language (invited talk) , 2015, DBPL.

[5]  Guillermo Palma,et al.  GRAPHIUM: Visualizing Performance of Graph and RDF Engines on Linked Data , 2013, International Semantic Web Conference.

[6]  Jeff Heflin,et al.  LUBM: A benchmark for OWL knowledge base systems , 2005, J. Web Semant..

[7]  Raghunath Othayoth Nambiar,et al.  Transaction Processing Performance Council (TPC): State of the Council 2010 , 2010, TPCTC.

[8]  Christian Bizer,et al.  The Berlin SPARQL Benchmark , 2009, Int. J. Semantic Web Inf. Syst..

[9]  Axel-Cyrille Ngonga Ngomo,et al.  HOBBIT: Holistic Benchmarking of Big Linked Data , 2016, ERCIM News.

[10]  Harsh Thakkar Towards an Open Extensible Framework for Empirical Benchmarking of Data Management Solutions: LITMUS , 2017, ESWC.

[11]  Toyotaro Suzumura,et al.  XGDBench: A benchmarking platform for graph stores in exascale clouds , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[12]  Muhammad Saleem,et al.  LSQ: The Linked SPARQL Queries Dataset , 2015, SEMWEB.

[13]  Georg Lausen,et al.  SP^2Bench: A SPARQL Performance Benchmark , 2008, 2009 IEEE 25th International Conference on Data Engineering.

[14]  M. Tamer Özsu,et al.  Diversified Stress Testing of RDF Data Management Systems , 2014, SEMWEB.

[15]  Maria-Esther Vidal,et al.  Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin , 2017, DEXA.

[16]  Muhammad Saleem,et al.  FEASIBLE: A Feature-Based SPARQL Benchmark Generation Framework , 2015, SEMWEB.

[17]  Olaf Hartig,et al.  Reconciliation of RDF* and Property Graphs , 2014, ArXiv.

[18]  Josep-Lluís Larriba-Pey,et al.  Survey of Graph Database Performance on the HPC Scalable Graph Analysis Benchmark , 2010, WAIM Workshops.

[19]  Josep-Lluís Larriba-Pey,et al.  The linked data benchmark council: a graph and RDF industry benchmarking effort , 2014, SGMD.

[20]  Axel-Cyrille Ngonga Ngomo,et al.  Automatic Generation of Benchmarks for Entity Recognition and Linking , 2017, ArXiv.

[21]  E. Prud hommeaux,et al.  SPARQL query language for RDF , 2011 .

[22]  Jens Lehmann,et al.  DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data , 2011, SEMWEB.