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A Survey on Big Data Analytics Solutions Deployment

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 11681)


There are widespread and increasing interest in big data analytics (BDA) solutions to enable data collection, transformation, and predictive analyses. The development and operation of BDA application involve business innovation, advanced analytics and cutting-edge technologies which add new complexities to the traditional software development. Although there is a growing interest in BDA adoption, successful deployments are still scarce (a.k.a., the “Deployment Gap” phenomenon). This paper reports an empirical study on BDA deployment practices, techniques and tools in the industry from both the software architecture and data science perspectives to understand research challenges that emerge in this context. Our results suggest new research directions to be tackled by the software architecture community. In particular, competing architectural drivers, interoperability, and deployment procedures in the BDA field are still immature or have not been adopted in practice.

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  1. 1.


  1. Chapman, P., et al.: CRISP-DM 1.0 step-by-step data mining guide. Technical report, The CRISP-DM consortium, August 2000

    Google Scholar 

  2. IBM: Foundational methodology for data science (2015). Accessed 11 July 2017

  3. Chen, H.M., Kazman, R., Matthes, F.: Demystifying big data adoption: beyond IT fashion and relative advantage. In: Twentieth DIGIT Workshop, Texas, US, pp. 1–14 (2015)

    Google Scholar 

  4. Chen, H.M., Schütz, R., Kazman, R., Matthes, F.: How Lufthansa capitalized on big data for business model renovation. MIS Q. Exec. 1615(14), 299–320 (2017)

    Google Scholar 

  5. Kitchenham, B.A., Pfleeger, S.L.: Personal opinion surveys. In: Shull, F., Singer, J., Sjøberg, D.I.K. (eds.) Guide to Advanced Empirical Software Engineering, pp. 63–92. Springer, London (2008).

    CrossRef  Google Scholar 

  6. Rexer, K.: 2013 data miner survey. Technical report, Rexer Analytics (2013)

    Google Scholar 

  7. Rexer, K., Gearan, P., Allen, H.: 2015 data science survey. Technical report, Rexer Analytics (2016)

    Google Scholar 

  8. Dataiku: building production-ready predictive analytics (2017). Accessed 11 July 2017

  9. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big bata, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21 (2011)

    Google Scholar 

  10. Easterbrook, S., Singer, J., Storey, M.A., Damian, D.: Selecting empirical methods for software engineering research. In: Shull, F., Singer, J., Sjøberg, D.I.K. (eds.) Guide to Advanced Empirical Software Engineering, pp. 285–311. Springer, London (2008).

    CrossRef  Google Scholar 

  11. Katz, R.L.: El Observatorio de la Economía Digital de Colombia. Technical report, Ministerio de Tecnologías de la Información y las Comunicaciones (2017)

    Google Scholar 

  12. Castellanos, C., Correal, D., Rodriguez, J.-D.: Executing architectural models for big data analytics. In: Cuesta, C.E., Garlan, D., Pérez, J. (eds.) ECSA 2018. LNCS, vol. 11048, pp. 364–371. Springer, Cham (2018).

    CrossRef  Google Scholar 

  13. Lechevalier, D., Ak, R., Lee, Y.T., Hudak, S., Foufou, S.: A neural network meta-model and its application for manufacturing. In: 2015 IEEE International Conference on Big Data (2015)

    Google Scholar 

  14. Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empir. Softw. Eng. 14(2), 131 (2008)

    CrossRef  Google Scholar 

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This research is supported by Fulbright Colombia and the Center of Excellence and Appropriation in Big Data and Data Analytics (CAOBA), supported by the Ministry of Information Technologies and Telecommunications of the Republic of Colombia (MinTIC) through the Colombian Administrative Department of Science, Technology, and Innovation (COLCIENCIAS) within contract No. FP44842-anexo46-2015.

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Correspondence to Camilo Castellanos .

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Castellanos, C., Pérez, B., Varela, C.A., Villamil, M.d.P., Correal, D. (2019). A Survey on Big Data Analytics Solutions Deployment. In: Bures, T., Duchien, L., Inverardi, P. (eds) Software Architecture. ECSA 2019. Lecture Notes in Computer Science(), vol 11681. Springer, Cham.

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  • Print ISBN: 978-3-030-29982-8

  • Online ISBN: 978-3-030-29983-5

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