Beyond TPC-DS, a benchmark for Big Data OLAP systems (BDOLAP-Bench)
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Título: | Beyond TPC-DS, a benchmark for Big Data OLAP systems (BDOLAP-Bench) |
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Autor/es: | Tardío, Roberto | Maté, Alejandro | Trujillo, Juan |
Grupo/s de investigación o GITE: | Lucentia |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Big Data OLAP | Benchmarking | Data modelling | Kylin | Druid |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 23-feb-2022 |
Editor: | Elsevier |
Cita bibliográfica: | Future Generation Computer Systems. 2022, 132: 136-151. https://doi.org/10.1016/j.future.2022.02.015 |
Resumen: | Online Analytical Processing (OLAP) systems with Big Data support allow storing tables of up to tens of billions of rows or terabytes of data. At the same time, these tools allow the execution of analytical queries with interactive response times, thus making them suitable for the implementation of Business Intelligence applications. However, since there can be significant differences in query and data loading performance between current Big Data OLAP tools, it is worthwhile to evaluate and compare them using a benchmark. But we identified that none of the existing approaches are really suitable for this type of system. To address this, in this research we propose a new benchmark specifically designed for Big Data OLAP systems and based on the widely adopted TPC-DS benchmark. To overcome TPC-DS inadequacy, we propose (i) a set of transformations to support the implementation of its sales data mart on any current Big Data OLAP system, (ii) a choice of 16 genuine OLAP queries, and (iii) an improved data maintenance performance metric. Moreover, we validated our benchmark through its implementation on four representative systems. |
Patrocinador/es: | This research has been funded by the AETHER-UA project (PID2020-112540RB-C43) of the Spanish Ministry of Science and Innovation and by the BALLADEER project (PROMETEO/2021/088), funded by the Conselleria d’Innovació, Universitats, Ciència i Societat Digital. |
URI: | http://hdl.handle.net/10045/121892 |
ISSN: | 0167-739X (Print) | 1872-7115 (Online) |
DOI: | 10.1016/j.future.2022.02.015 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.future.2022.02.015 |
Aparece en las colecciones: | INV - LUCENTIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Tardio_etal_2022_FutureGeneratComputSyst_accepted.pdf | Accepted Manuscript (acceso abierto) | 1,46 MB | Adobe PDF | Abrir Vista previa |
Tardio_etal_2022_FutureGeneratComputSyst_final.pdf | Versión final (acceso restringido) | 1,56 MB | Adobe PDF | Abrir Solicitar una copia |
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