Skip to main content
SpringerLink
Log in

Big Data Software Architectures: An Updated Review

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13375))

Included in the following conference series:

Abstract

Big Data usually refers to the unprecedented growth of data and associated processes to gather, store, process, and analyze them to provide organizations and users with useful insights and information. The intrinsic complexity and characteristics of systems handling Big Data require software architectures as founded drivers for these systems to meet functional and quality requirements. In light of the relevant role of software architectures for Big Data systems, we investigate the current state of the art of Big Data software architectures. This paper presents the results of a systematic mapping study that updates existing literature reviews on this topic. We selected and analyzed 23 primary studies published in the last five years. We identified 11 architecture-related quality requirements and six architectural modules relevant to the design of software architectures for Big Data systems, besides analyzing whether existing proposals of reference architectures comply with these requirements and modules. We expect the results presented in this paper can provide a continuous update of the state of the art while highlighting essential concerns in the design of software architectures for Big Data systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://parsif.al/.

References

  1. Avci, C., Tekinerdogan, B., Athanasiadis, I.N.: Software architectures for Big Data: a systematic literature review. Big Data Anal. 5 (2020). https://doi.org/10.1186/s41044-020-00045-1

  2. Beyer, M., Laney, D.: The importance of ‘Big Data’: A definition. Technical report, Gartner, USA, June 2012

    Google Scholar 

  3. Cloutier, R., Muller, G., Verma, D., Nilchiani, R., Hole, E., Bone, M.: The concept of reference architectures. Syst. Eng. 13(1), 14–27 (2010). https://doi.org/10.1002/sys.20129

    Article  Google Scholar 

  4. Garcés, L., et al.: Three decades of software reference architectures: a systematic mapping study. J. Syst. Softw. 179 (2021). https://doi.org/10.1016/j.jss.2021.111004

  5. Garner, P., et al.: When and how to update systematic reviews: Consensus and checklist. BMJ 354 (2016). https://doi.org/10.1136/bmj.i3507

  6. Hai, R., Geisler, S., Quix, C.: Constance: an intelligent data lake system. In: Proceedings of the 2016 International Conference on Management of Data, pp. 2097–2100. ACM, USA (2016). https://doi.org/10.1145/2882903.2899389

  7. ISO/IEC 25010: Systems and software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE) - System and software quality models. ISO, Switzerland (2011)

    Google Scholar 

  8. Janssen, M., Brous, P., Estevez, E., Barbosa, L.S., Janowski, T.: Data governance: Organizing data for trustworthy Artificial Intelligence. Govern. Inf. Q. 37(3) (2020). https://doi.org/10.1016/j.giq.2020.101493

  9. Kim, H.Y., Cho, J.S.: Data Governance Framework for Big Data implementation with a case of Korea. In: Proceedings of the 2017 IEEE International Congress on Big Data (2017). https://doi.org/10.1109/bigdatacongress.2017.56

  10. Kitchenham, B.A., Budgen, D., Brereton, P.: Evidence-Based Software Engineering and systematic reviews. Chapman and Hall/CRC Press, USA (2016)

    Google Scholar 

  11. Kumar, V.D., Alencar, P.: Software Engineering for Big Data projects: domains, methodologies and gaps. In: Proceedings of the 2016 IEEE International Conference on Big Data, pp. 2886–2895. IEEE, USA (2016). https://doi.org/10.1109/bigdata.2016.7840938

  12. Laney, D.: 3D data management: Controlling Data Volume, Velocity, and Variety. Technical report, META Group, USA, February 2001

    Google Scholar 

  13. Mendes, E., Wohlin, C., Felizardo, K., Kalinowski, M.: When to update systematic literature reviews in Software Engineering. J. Syst. Softw. 167 (2020). https://doi.org/10.1016/j.jss.2020.110607

  14. Montero, O., Crespo, Y., Piatini, M.: Big data quality models: a systematic mapping study. In: Paiva, A.C.R., Cavalli, A.R., Ventura Martins, P., Pérez-Castillo, R. (eds.) QUATIC 2021. CCIS, vol. 1439, pp. 416–430. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85347-1_30

    Chapter  Google Scholar 

  15. Nakagawa, E.Y., Oquendo, F., Maldonado, J.C.: Reference architectures. In: Oussalah, M.C. (ed.) Software Architecture 1, pp. 55–82. ISTE/John Wiley & Sons Inc., United Kingdom (2014). https://doi.org/10.1002/9781118930960.ch2

  16. NewVantage: Big Datra and AI Executive Survey 2021: Executive summary of findings. techreport, NewVantage Partners LLC, USA, January 2021

    Google Scholar 

  17. Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in Software Engineering. In: Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, pp. 68–77. British Computer Society, United Kingdom (2008). https://doi.org/10.14236/ewic/ease2008.8

  18. Petersen, K., Vakkalanka, S., Kuzniarz, L.: Guidelines for conducting systematic mapping studies in Software Engineering: an update. Inf. Softw. Technol. 64, 1–18 (2015). https://doi.org/10.1016/j.infsof.2015.03.007

    Article  Google Scholar 

  19. Rahman, M.S., Reza, H.: Systematic mapping study of non-functional requirements in Big Data system. In: Proceedings of the 2020 IEEE International Conference on Electro Information Technology, pp. 025–031. IEEE, USA (2020). https://doi.org/10.1109/eit48999.2020.9208288

  20. Sena, B., Allian, A.P., Nakagawa, E.Y.: Characterizing Big Data software architectures: a systematic mapping study. In: Proceedings of the 11th Brazilian Symposium on Software Components, Architectures, and Reuse. ACM, USA (2017). https://doi.org/10.1145/3132498.3132510

  21. Sena, B., Garcés, L., Allian, A.P., Nakagawa, E.Y.: Investigating the applicability of architectural patterns in Big Data systems. In: Proceedings of the 25th Conference on Pattern Languages of Programs. ACM, USA (2018)

    Google Scholar 

  22. Wahyudi, A., Kuk, G., Janssen, M.: A process pattern model for tackling and improving big data quality. Inf. Syst. Front. 20(3), 457–469 (2018). https://doi.org/10.1007/s10796-017-9822-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Federal University of Rio Grande do Norte, Natal, Brazil

    Tiago Vinícius Remígio da Costa, Everton Cavalcante & Thais Batista

Authors
  1. Tiago Vinícius Remígio da Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Everton Cavalcante
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thais Batista
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Everton Cavalcante .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

da Costa, T.V.R., Cavalcante, E., Batista, T. (2022). Big Data Software Architectures: An Updated Review. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10522-7_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10521-0

  • Online ISBN: 978-3-031-10522-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation