A Proposed Architecture for IoT Big Data Analysis in Smart Supply Chain Fields

  • Fabián-Vinicio Constante-NicolaldeEmail author
  • Jorge-Luis Pérez-MedinaEmail author
  • Paulo Guerra-Terán
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)


The growth of large amounts of data in the last decade from Cloud Computing, Information Systems, and Digital Technologies with an increase in the production and miniaturization of Internet of Things (IoT) devices. However, these data without analytical power are not useful in any field. Concentration efforts at multiple levels are required for the extraction of knowledge and decision-making being the “Big Data Analysis” an area increasingly challenging. Numerous analysis solutions combining Big Data and IoT have allowed people to obtain valuable information. Big Data requires a certain complexity. Small Data is emerging as a more efficient alternative, since it combines structured and unstructured data that can be measured in Gigabytes, Peta bytes or Terabytes, forming part of small sets of specific IoT attributes. This article presents an architecture for the analysis of data generated by IoT. The proposed solution allows the extraction of knowledge, focusing on the case of specific use of the “Smart Supply Chain fields”.


Big Data Analysis Internet of Things Data mining Hadoop Radio frequency identification 



This work was possible thanks “Universidad de Las Américas” from Ecuador for the financing of research and Leiria Polytechnic Institute from Portugal.


  1. 1.
    Puranam, K., Tavana, M.: Handbook of Research on Organizational Transformations through Big Data Analytics, p. 109 (2012)Google Scholar
  2. 2.
    Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017)CrossRefGoogle Scholar
  3. 3.
    Constante Nicolalde, F., Silva, F., Herrera, B., Pereira, A.: Big data analytics in IOT: challenges, open research issues and tools. Adv. Intell. Syst. Comput. 746, 775–788 (2018)Google Scholar
  4. 4.
    Rowe, S., Pournader, M.: How big data is shaping the supply chains of tomorrow. KPMG, Supply Chain Big Data Series, no. March, pp. 1–16 (2017)Google Scholar
  5. 5.
    Kazman, R., Bass, L., Clements, P.: Software Architecture in Practice. Addison-Wesley Professional, Westford (2012)Google Scholar
  6. 6.
    Lele, A.: Big data: related technologies, challenges and future prospects. Smart Innov. Syst. Technol. 132, 155–165 (2019)CrossRefGoogle Scholar
  7. 7.
    Radhakrishnan, M., Sen, S., Vigneshwaran, S., Misra, A., Balan, R.: IoT+Small Data: transforming in-store shopping analytics & services. In: 2016 8th International Conference on Communication Systems and Networks, COMSNETS 2016, no. January (2016)Google Scholar
  8. 8.
    Ganesh, E.N.: Development of SMART CITY Using IOT and BIG Data. Int. J. Comput. Tech. 4(1), 36–37 (2017)MathSciNetGoogle Scholar
  9. 9.
    Biswas, S., Sen, J.: A proposed architecture for big data driven supply chain analytics. SSRN Electron. J., 1–24 (2016)Google Scholar
  10. 10.
    Bashir, M.R., Gill, A.Q.: Towards an IoT big data analytics framework: smart buildings systems. In: Proceedings of 2016 IEEE 18th International Conference on High Performance Computing and Communications. IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and System, pp. 1325–1332 (2016)Google Scholar
  11. 11.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of 6th Symposium on Operating Systems Design and Implementation, pp. 137–149 (2004)Google Scholar
  12. 12.
    Deploying Spark, HPE Elastic Platform, Big Data Analytics. Spark – a modern data processing framework for cross platform analytics Deploying Spark on HPE Elastic Platform for Big Data (2014)Google Scholar
  13. 13.
    Prakash, M., Padmapriy, G., Kumar, M.V.: A review on machine learning big data using R. In: Proceedings of International Conference on Inventive Communication and Computational Technologies. ICICCT 2018, no. Icicct, pp. 1873–1877 (2018)Google Scholar
  14. 14.
    Nagdive, A.S., Tugnayat, R.M.: A review of Hadoop ecosystem for bigdata. Int. J. Comput. Appl. 180(14), 35–40 (2018)Google Scholar
  15. 15.
    Ingersoll, G.: Introducing apache mahout: scalable, commercial friendly machine learning for building intelligent applications. White Paper, IBM Developer Works, pp. 1–18 (2009)Google Scholar
  16. 16.
    Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: distributed data-parallel programs from sequential building blocks. In: ACM SIGOPS Operating Systems Review, pp. 59–72 (2007)CrossRefGoogle Scholar
  17. 17.
    Chen, C.L.P., Zhang, C.-Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)CrossRefGoogle Scholar
  18. 18.
    Apache Software Foundation: Apache Drill Brings SQL-Like, Ad Hoc Query Capabilities to Big Data (2014). Accessed 03 Mar 2018
  19. 19.
    Tableau Software: Build your big data platform with Tableau and Cloudera (2018). Accessed 10 Mar 2019
  20. 20.
    Rajagopalan, N.: Big Data Analytics with Clickstream.
  21. 21.
    Apache Software Foundation: Apache Impala Overview. Accessed 10 Mar 2019
  22. 22.
    Cloudera. Introducing Morphlines: The Easy Way to Build and Integrate ETL Apps for Hadoop (2019). Accessed 31 Jan 2019
  23. 23.
    Constante, F., Silva, F., Pereira, A.: DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS. Mendeley (2019). Accessed 12 Mar 2019
  24. 24.
    DataSciencePlus: How to Perform a Logistic Regression in R.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Intelligent and Interactive Systems Lab (SI2-Lab)Universidad de Las AméricasQuitoEcuador
  2. 2.School of Technology and ManagementPolytechnic Institute of LeiriaLeiriaPortugal

Personalised recommendations