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A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques

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  • معلومة اضافية
    • Contributors:
      Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos; Lucentia
    • بيانات النشر:
      MDPI
    • الموضوع:
      2020
    • Collection:
      RUA - Repositorio Institucional de la Universidad de Alicante
    • نبذة مختصرة :
      In recent years, several new technologies have enabled OLAP processing over Big Data sources. Among these technologies, we highlight those that allow data pre-aggregation because of their demonstrated performance in data querying. This is the case of Apache Kylin, a Hadoop based technology that supports sub-second queries over fact tables with billions of rows combined with ultra high cardinality dimensions. However, taking advantage of data pre-aggregation techniques to designing analytic models for Big Data OLAP is not a trivial task. It requires very advanced knowledge of the underlying technologies and user querying patterns. A wrong design of the OLAP cube alters significantly several key performance metrics, including: (i) the analytic capabilities of the cube (time and ability to provide an answer to a query), (ii) size of the OLAP cube, and (iii) time required to build the OLAP cube. Therefore, in this paper we (i) propose a benchmark to aid Big Data OLAP designers to choose the most suitable cube design for their goals, (ii) we identify and describe the main requirements and trade-offs for effectively designing a Big Data OLAP cube taking advantage of data pre-aggregation techniques, and (iii) we validate our benchmark in a case study. ; This work has been funded by the ECLIPSE project (RTI2018-094283-B-C32) from the Spanish Ministry of Science, Innovation and Universities.
    • ISSN:
      2076-3417
    • Relation:
      https://doi.org/10.3390/app10238674; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094283-B-C32; Tardío R, Maté A, Trujillo J. A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques. Applied Sciences. 2020; 10(23):8674. https://doi.org/10.3390/app10238674; http://hdl.handle.net/10045/112029
    • الرقم المعرف:
      10.3390/app10238674
    • الدخول الالكتروني :
      http://hdl.handle.net/10045/112029
      https://doi.org/10.3390/app10238674
    • Rights:
      © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). ; info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.5711F5FA