Decision-Support for Selecting Big Data Reference Architectures

  • Matthias VolkEmail author
  • Sascha BosseEmail author
  • Dennis BischoffEmail author
  • Klaus TurowskiEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)


In recent years, big data systems are getting increasingly complex and require a deep domain specific knowledge. Although a multitude of reference architectures exist, it remains challenging to identify the most suitable approach for a specific use case scenario. To overcome this problem and to provide a decision support, the design science research methodology is used. By an initial literature review process and the application of the software architecture comparison analysis method, currently established big data reference architectures are identified and compared to each other. Finally, an Analytic Hierarchy Process as the main artefact is proposed, demonstrated and evaluated on a real world use-case.


Big data Reference architectures Analytic hierarchy processing Multi criteria decision making 


  1. 1.
    Dresner Advisory Services, LLC: Big Data Analytics Market Study. Accessed 5 Dec 2018
  2. 2.
    NIST Big Data Interoperability Framework. Volume 1, Definitions. National Institute of Standards and TechnologyGoogle Scholar
  3. 3.
    Volk, M., Pohl, M., Turowski, K.: Classifying big data technologies - an ontology-based approach. In: 24th Americas Conference on Information 2018. Association for Information Systems, New Orleans, LA, USA (2018)Google Scholar
  4. 4.
    ISO/IEC/IEEE 42010:2011: Systems and software engineering — Architecture description. Geneva, CH (2011)Google Scholar
  5. 5.
    Marz, N.: How to beat the CAP theorem. Accessed 5 Dec 2018
  6. 6.
    Lněnička, M.: AHP model for the big data analytics platform selection. Acta Informatica Pragensia 4, 108–121 (2015)CrossRefGoogle Scholar
  7. 7.
    Nadal, S., et al.: A software reference architecture for semantic-aware Big Data systems. Inf. Softw. Technol. 90, 75–92 (2017)CrossRefGoogle Scholar
  8. 8.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28, 75–105 (2004)CrossRefGoogle Scholar
  9. 9.
    Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manage. Inf. Syst. 24, 45–77 (2007)CrossRefGoogle Scholar
  10. 10.
    Webster, J., Watson, R.T.: Guest Editorial: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26, xiii–xxii (2002)Google Scholar
  11. 11.
    Stoermer, C., Bachmann, F., Verhoef, C.: SACAM: The Software Architecture Comparison Analysis Method. Pittsburgh, Pennsylvania (2003)Google Scholar
  12. 12.
    Azarmi, B.: Scalable Big Data Architecture. A Practitioner’s Guide to Choosing Relevant Big Data Architecture. Apress, Berkeley (2016)Google Scholar
  13. 13.
    Haunschild, R., Hug, S.E., Brändle, M.P., Bornmann, L.: The number of linked references of publications in Microsoft Academic in comparison with the Web of Science. Scientometrics 114, 367–370 (2018)CrossRefGoogle Scholar
  14. 14.
    Kiran, M., Murphy, P., Monga, I., Dugan, J., Baveja, S.S.: Lambda architecture for cost-effective batch and speed big data processing. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2785–2792. IEEE (2015)Google Scholar
  15. 15.
    Marz, N., Warren, J.: Big Data. Principles and Best Practices of Scalable Real-Time Data Systems. Manning, Shelter Island (2015)Google Scholar
  16. 16.
    Kreps, J.: Questioning the Lambda Architecture. The Lambda Architecture has its merits, but alternatives are worth exploring. Accessed 5 Dec 2018
  17. 17.
    Zschörnig, T., Wehlitz, R., Franczyk, B.: A personal analytics platform for the Internet of Things - implementing Kappa Architecture with microservice-based stream processing. In: Proceedings of the 19th International Conference on Enterprise Information Systems, pp. 733–738. SCITEPRESS (2017)Google Scholar
  18. 18.
    Wingerath, W., Gessert, F., Friedrich, S., Ritter, N.: Real-time stream processing for Big Data. it Inf. Technol. 58(4), 186–194 (2016)Google Scholar
  19. 19.
    Martínez-Prieto, M.A., Cuesta, C.E., Arias, M., Fernández, J.D.: The solid architecture for real-time management of big semantic data. Future Gener. Comput. Syst. 47, 62–79 (2015)CrossRefGoogle Scholar
  20. 20.
    Cuesta, C.E., Martínez-Prieto, M.A., Fernández, J.D.: Towards an architecture for managing big semantic data in real-time. In: Drira, K. (ed.) ECSA 2013. LNCS, vol. 7957, pp. 45–53. Springer, Heidelberg (2013). Scholar
  21. 21.
    Pääkkönen, P., Pakkala, D.: Reference architecture and classification of technologies, products and services for big data systems. Big Data Res. 2, 166–186 (2015)CrossRefGoogle Scholar
  22. 22.
    Demchenko, Y., de Laat, C., Membrey, P.: Defining architecture components of the Big Data Ecosystem. In: 2014 International Conference on Collaboration Technologies and Systems (CTS), pp. 104–112. IEEE (2014)Google Scholar
  23. 23.
    Kumar, A., et al.: A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew. Sustain. Energy Rev. 69, 596–609 (2017)CrossRefGoogle Scholar
  24. 24.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)Google Scholar
  25. 25.
    Alves, C., Finkelstein, A.: Challenges in COTS decision-making. In: Tortora, G. (ed.) Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering, p. 789. ACM, New York (2002)Google Scholar
  26. 26.
    Alebrahim, A.: Bridging the Gap between Requirements Engineering and Software Architecture. Springer Fachmedien Wiesbaden, Wiesbaden (2017). Scholar
  27. 27.
    Cloutier, R., Muller, G., Verma, D., Nilchiani, R., Hole, E., Bone, M.: The concept of reference architectures. Syst. Eng. 13(1), 14–27 (2009)Google Scholar
  28. 28.
    Anwer, H., Fatima, K.N., Saqib, N.A.: Fraud-sentient security architecture for improved telephone banking experience. In: IEEE 7th Annual Information 2016, pp. 1–7 (2016)Google Scholar
  29. 29.
    Schönebeck, B., Skottke, E.-M.: „Big Data“ und Kundenzufriedenheit: Befragungen versus Social Media? In: Gansser, O., Krol, B. (eds.) Moderne Methoden der Marktforschung. F, pp. 229–245. Springer, Wiesbaden (2017). Scholar
  30. 30.
    Huici, F., Di Pietro, A., Trammell, B., Gomez Hidalgo, J.M., Martinez Ruiz, D., d’Heureuse, N.: Blockmon: a high-performance composable network traffic measurement system. In: Eggert, L. (ed.) Proceedings of the ACM SIGCOMM 2012 Conference, p. 79. ACM Press, New York (2012)Google Scholar
  31. 31.
    Daily, J., Peterson, J.: Predictive maintenance: how big data analysis can improve maintenance. In: Richter, K., Walther, J. (eds.) Supply Chain Integration Challenges in Commercial Aerospace, pp. 267–278. Springer, Cham (2017). Scholar
  32. 32.
    Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big data. Deep learning for financial sentiment analysis. J. Big Data 5, 210 (2018)CrossRefGoogle Scholar
  33. 33.
    Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1, 83–98 (2008)Google Scholar
  34. 34.
    Lambda at Weather Scale – Databricks. Accessed 4 Sept 2018
  35. 35.
    Wang, Y.-M., Luo, Y.: On rank reversal in decision analysis. Math. Comput. Model. 49, 1221–1229 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Otto-von-Guericke-University MagdeburgMagdeburgGermany

Personalised recommendations