Geodesic
Improving the efficiency of Clusterix-like dbms for big data analytical processing
Informacionnye tehnologii i vyčislitelnye sistemy, no. 4 (2019), pp. 43-59 Cet article a éte moissonné depuis la source Math-Net.Ru

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Commercial OLAP-systems are economically unavailable for organizations with limited financial capabilities. Analytical processing large amounts of data in these organizations can be accomplished using open source software systems on a cost-effective cluster platform. Previously created Clusterix-like DBMS were not efficient enough according to the «performance/cost» criterion. With a view to the enhance the effectiveness of such systems in the article considers their further development with a focus on a full load of processor cores and the using GPU acceleration (systems Clusterix-N, N – from New) up to the development of a system comparable in efficiency to the open source system Spark, which is currently considered the most promising. The development methodology was based on the constructive system modeling methodology.
Keywords: analytic processing of significant data volumes, open source software systems on a cluster platform, increasing the efficiency of Clusterix-like DBMS, full loading of processor cores, full load of processor cores, GPU acceleration, comparison with Spark, accepted methodology. 
@article{ITVS_2019_4_a4,
     author = {R. K. Klassen and V. A. Raikhlin},
     title = {Improving the efficiency of {Clusterix-like} dbms for big data analytical processing},
     journal = {Informacionnye tehnologii i vy\v{c}islitelnye sistemy},
     pages = {43--59},
     year = {2019},
     number = {4},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ITVS_2019_4_a4/}
}
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R. K. Klassen; V. A. Raikhlin. Improving the efficiency of Clusterix-like dbms for big data analytical processing. Informacionnye tehnologii i vyčislitelnye sistemy, no. 4 (2019), pp. 43-59. http://geodesic.mathdoc.fr/item/ITVS_2019_4_a4/