skip to main content
10.1145/3401895.3401899acmotherconferencesArticle/Chapter ViewAbstractPublication Pageseatis-orgConference Proceedingsconference-collections
research-article

ROLAP DW transformation proposal for OLAP architecture in NoSQL database

Published:29 January 2021Publication History

ABSTRACT

This paper aims to present a case study related to the migration of a ROLAP architecture Data Warehouse of the University of Brasilia to an OLAP architecture in the NoSQL DB family of columns. This migration starts from the need to study new paradigms of architectures for decision support systems due to the new reality of the problems generated by the use of Big Data in the present moment at the university. We made two approaches: the first one by migrating to the Cassandra DBMS and the second one by migrating to Apache Hive. State of the art was made from Web of Science and we used the TEMAC methodology. We made a transformation of integrating all dimensions and table of facts into a single table for both Cassandra and Hive and we made a comparison by running two queries from these tables. Besides that, we also made a qualitative analysis, addressing the advantages and disadvantages of each approach. In the end, we concluded that Apache Hive should be the best choice for the University of Brasilia.

References

  1. Z. Bicevska and I. Oditis. Towards nosql-based data warehouse solutions. Procedia Computer Science, 104:104--111, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Boussahoua, O. Boussaid, and F. Bentayeb. Logical schema for data warehouse on column-oriented nosql databases. In International Conference on Database and Expert Systems Applications, pages 247--256. Springer, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  3. J. Camacho-Rodríguez, A. Chauhan, A. Gates, E. Koifman, O. O'Malley, V. Garg, Z. Haindrich, S. Shelukhin, P. Jayachandran, S. Seth, et al. Apache hive: From mapreduce to enterprise-grade big data warehousing. arXiv preprint arXiv:1903.10970, 2019.Google ScholarGoogle Scholar
  4. R. Cattell. Scalable sql and nosql data stores. Acm Sigmod Record, 39(4):12--27, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. L. Chao, C. Li, F. Liang, X. Lu, and Z. Xu. Accelerating apache hive with mpi for data warehouse systems. In 2015 IEEE 35th International Conference on Distributed Computing Systems, pages 664--673. IEEE, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  6. S. Chaudhuri and U. Dayal. An overview of data warehousing and olap technology. ACM Sigmod record, 26(1):65--74, 1997.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Chevalier, M. El Malki, A. Kopliku, O. Teste, and R. Tournier. Benchmark for olap on nosql technologies. 2015.Google ScholarGoogle Scholar
  8. M. Chevalier, M. El Malki, A. Kopliku, O. Teste, and R. Tournier. How can we implement a multidimensional data warehouse using nosql? In International Conference on Enterprise Information Systems, pages 108--130. Springer, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  9. M. Chevalier, M. El Malki, A. Kopliku, O. Teste, and R. Tournier. Implementation of multidimensional databases in column-oriented nosql systems. In East European Conference on Advances in Databases and Information Systems, pages 79--91. Springer, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  10. M. Chevalier, M. El Malki, A. Kopliku, O. Teste, and R. Tournier. Document-oriented models for data warehouses. 2016.Google ScholarGoogle Scholar
  11. A. Cuzzocrea, L. Bellatreche, I.-Y. Song, et al. Data warehousing and olap over big data: current challenges and future research directions. In DOLAP, volume 13, pages 67--70, 2013.Google ScholarGoogle Scholar
  12. E. Dede, M. Govindaraju, D. Gunter, R. S. Canon, and L. Ramakrishnan. Performance evaluation of a mongodb and hadoop platform for scientific data analysis. In Proceedings of the 4th ACM workshop on Scientific cloud computing, pages 13--20. ACM, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. K. Dehdouh. Building olap cubes from columnar nosql data warehouses. In International Conference on Model and Data Engineering, pages 166--179. Springer, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  14. K. Dehdouh, F. Bentayeb, O. Boussaid, and N. Kabachi. Columnar nosql cube: Agregation operator for columnar nosql data warehouse. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 3828--3833. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  15. K. Dehdouh, F. Bentayeb, O. Boussaid, and N. Kabachi. Using the column oriented nosql model for implementing big data warehouses. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), page 469. The Steering Committee of The World Congress in Computer Science, Computer, 2015.Google ScholarGoogle Scholar
  16. M. Golfarelli, D. Maio, and S. Rizzi. The dimensional fact model: A conceptual model for data warehouses. International Journal of Cooperative Information Systems, 7(02n03):215--247, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  17. Y. Hu, V. Y. Gunapati, P. Zhao, D. Gordon, N. R. Wheeler, M. A. Hossain, T. J. Peshek, L. S. Bruckman, G.-Q. Zhang, and R. H. French. A nonrelational data warehouse for the analysis of field and laboratory data from multiple heterogeneous photovoltaic test sites. IEEE Journal of Photovoltaics, 7(1):230--236, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  18. R. Kimball and M. Ross. The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. N. Leavitt. Will nosql databases live up to their promise? Computer, 43(2):12--14, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Y. Liu and T. M. Vitolo. Graph data warehouse: Steps to integrating graph databases into the traditional conceptual structure of a data warehouse. In 2013 IEEE International Congress on Big Data, pages 433--434. IEEE, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. K. Ma and R. Sun. Introducing websocket-based real-time monitoring system for remote intelligent buildings. International Journal of Distributed Sensor Networks, 9(12):867693, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  22. K. Ma and B. Yang. Introducing extreme data storage middleware of schema-free document stores using mapreduce. International Journal of Ad Hoc and Ubiquitous Computing, 20(4):274--284, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. K. Ma and B. Yang. Column access-aware in-stream data cache with stream processing framework. Journal of Signal Processing Systems, 86(2--3):191--205, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. M. MARIANO and M. S. ROCHA. Revisão da literatura: Apresentação de uma abordagem integradora. In XXVI Congreso Internacional de la Academia Europea de Dirección y Economía de la Empresa (AEDEM), Reggio Calabria, volume 26, 2017.Google ScholarGoogle Scholar
  25. K. Psiuk-Maksymowicz, A. Płaczek, R. Jaksik, S. Student, D. Borys, D. Mrozek, K. Fujarewicz, and A. Świerniak. A holistic approach to testing biomedical hypotheses and analysis of biomedical data. In Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery, pages 449--462. Springer, 2015.Google ScholarGoogle Scholar
  26. P. J. Sadalage and M. Fowler. NoSQL essencial: um guia conciso para o mundo emergente da persistência poliglota. Novatec Editora, 2013.Google ScholarGoogle Scholar
  27. M. Y. Santos, B. Martinho, and C. Costa. Modelling and implementing big data warehouses for decision support. Journal of Management Analytics, 4(2):111--129, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  28. S. Wang, I. Pandis, C. Wu, S. He, D. Johnson, I. Emam, F. Guitton, and Y. Guo. High dimensional biological data retrieval optimization with nosql technology. In BMC genomics, volume 15, page S3. BioMed Central, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  29. R. Yangui, A. Nabli, and F. Gargouri. Automatic transformation of data warehouse schema to nosql data base: comparative study. Procedia Computer Science, 96:255--264, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. H. Zhao and X. Ye. A practice of tpc-ds multidimensional implementation on nosql database systems. In Technology Conference on Performance Evaluation and Benchmarking, pages 93--108. Springer, 2013.Google ScholarGoogle Scholar

Index Terms

  1. ROLAP DW transformation proposal for OLAP architecture in NoSQL database

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      EATIS '20: Proceedings of the 10th Euro-American Conference on Telematics and Information Systems
      November 2020
      388 pages
      ISBN:9781450377119
      DOI:10.1145/3401895

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 January 2021

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate17of64submissions,27%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader