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Benchmark for OLAP on NoSQL Technologies

Authors :
Max Chevalier
Mohammed El Malki
Arlind Kopliku
Olivier Teste
Ronan Tournier
Systèmes d’Informations Généralisées (IRIT-SIG)
Institut de recherche en informatique de Toulouse (IRIT)
Université Toulouse 1 Capitole (UT1)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3)
Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP)
Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)
Université Fédérale Toulouse Midi-Pyrénées
Université Toulouse III - Paul Sabatier (UT3)
Capgemini [Toulouse]
Capgemini
Université Toulouse - Jean Jaurès (UT2J)
Centre National de la Recherche Scientifique - CNRS (FRANCE)
Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université Toulouse 1 Capitole - UT1 (FRANCE)
Capgemini (FRANCE)
Institut National Polytechnique de Toulouse - INPT (FRANCE)
Source :
Proceedings of RCIS 2015, 9th IEEE International Conference on Research Challenges in Information Science (IEEE RCIS 2015), 9th IEEE International Conference on Research Challenges in Information Science (IEEE RCIS 2015), May 2015, Athens, Greece. pp. 480-485, HAL
Publication Year :
2015
Publisher :
IEEE, 2015.

Abstract

International audience; The plethora of data warehouse solutions has created a need comparing these solutions using experimental benchmarks. Existing benchmarks rely mostly on the relational data model and do not take into account other models. In this paper, we propose an extension to a popular benchmark (the Star Schema Benchmark or SSB) that considers non-relational NoSQL models. To avoid data post-processing required for using this data with NoSQL systems, the data is generated in different formats. To exploit at best horizontal scaling, data can be produced in a distributed file system, hence removing disk or partition sizes as limit for the generated dataset. Experimental work proves improved performance of our new benchmark.

Details

Language :
French
Database :
OpenAIRE
Journal :
Proceedings of RCIS 2015, 9th IEEE International Conference on Research Challenges in Information Science (IEEE RCIS 2015), 9th IEEE International Conference on Research Challenges in Information Science (IEEE RCIS 2015), May 2015, Athens, Greece. pp. 480-485, HAL
Accession number :
edsair.dedup.wf.001..6f7e6bf870fb98d2fba16d1e7e62cad9