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A Proposed Big Data Architecture Using Data Lakes for Education Systems

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Emerging Trends in Intelligent Systems & Network Security (NISS 2022)

Abstract

Nowadays, educational data can be defined through the 3Vs of Big Data: volume, variety and velocity. Data sources produce massive and complex data, which makes knowledge extraction with traditional tools difficult for educational organizations. Indeed, the actual architecture of data warehouses do not possess the capability of storing and managing this huge amount of varied data. The same goes for analytical processes; which no longer satisfy business analysts; in terms of data availability and speed of execution of queries. These constraints have implied an evolution towards more modern architectures, integrating Big Data solutions capable of promoting smart learning to students. In this context, the present paper proposes a new big data architecture for education systems covering multiple data sources. Using this architecture, data is organized through a set of layers, starting with the management of the different data sources to their final consumption. The proposal approach includes data lake as a means of modernizing decision-making processes, in particular data warehouses and OLAP methods. It will be used as a means for data consolidation for the integration of heterogeneous data sources.

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References

  1. Janev, V.: Semantic intelligence in big data applications. In: Jain, S., Murugesan, S. (eds.) Smart Connected World, pp. 71–89. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76387-9_4

    Chapter  Google Scholar 

  2. Bimonte, S., Boussaid, O., Schneider, M., Ruelle, F.: Design and implementation of active stream data warehouses. In: Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering, pp. 288–311. IGI Global (2021)

    Google Scholar 

  3. Xu, L.D., Duan, L.: Big data for cyber physical systems in industry 4.0: a survey. Enterp. Inf. Syst. 13(2), 148–169 (2019)

    Google Scholar 

  4. Cebrián, G., Palau, R., Mogas, J.: The smart classroom as a means to the development of ESD methodologies. Sustainability 12(7), 3010 (2020)

    Google Scholar 

  5. Abdullayev, A.A.: System of information and communication technologies in the education. Sci. World Int. Sci. J. 2, 19–21 (2020)

    Google Scholar 

  6. Jha, S., Jha, M., O’Brien, L.: A step towards big data architecture for higher education analytics. In: 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, pp. 178–183. IEEE (2018)

    Google Scholar 

  7. Baig, M.I., Shuib, L., Yadegaridehkordi, E.: Big data in education: a state of the art, limitations, and future research directions. Int. J. Educ. Technol. High. Educ. 17(1), 1–23 (2020). https://doi.org/10.1186/s41239-020-00223-0

    Article  Google Scholar 

  8. Petricioli, L., Humski, L., Vrdoljak, B.: The challenges of NoSQL data warehousing. In: E-business Technologies Conference Proceedings, vol. 1, no. 1, pp. 44–48 (2021)

    Google Scholar 

  9. Wibowo, M., Sulaiman, S., Shamsuddin, S.M.: Machine learning in data lake for combining data silos. In: Tan, Y., Takagi, H., Shi, Y. (eds.) Data Mining and Big Data. Lecture Notes in Computer Science, vol. 10387, pp. 294–306. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61845-6_30

    Chapter  Google Scholar 

  10. Patel, J.: Bridging data silos using big data integration. Int. J. Database Manage. Syst. 11(3), 1–6 (2019)

    Article  Google Scholar 

  11. How, M.: The Modern Data Warehouse in Azure: Building with Speed and Agility on Microsoft’s Cloud Platform, 1st edn. Apress (2020)

    Google Scholar 

  12. Blažić, G., Poščić, P., Jakšić, D.: Data warehouse architecture classification. In: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1491–1495. IEEE (2017)

    Google Scholar 

  13. Santos, M.Y., Costa, C.: Big Data: Concepts, Warehousing and Analytics. River Publishers (2020)

    Google Scholar 

  14. Martins, A., Martins, P., Caldeira, F., Sá, F.: An evaluation of how big-data and data warehouses improve business intelligence decision making. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S., Orovic, I., Moreira, F. (eds.) Trends and Innovations in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol. 1159, pp. 609–619. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45688-7_61

    Chapter  Google Scholar 

  15. Sawadogo, P., Darmont, J.: On data lake architectures and metadata management. J. Intell. Inf. Syst. 56(1), 97–120 (2021). https://doi.org/10.1007/s10844-020-00608-7

    Article  Google Scholar 

  16. Oukhouya, L., Elhaddadi, A., Er-raha, B., Asri, H.: A generic metadata management model for heterogeneous sources in a data warehouse. In: E3S Web of Conferences, vol. 297, p. 01069. EDP Sciences (2021)

    Google Scholar 

  17. Munshi, A.A., Alhindi, A.: Big Data Platform for Educational Analytics. IEEE Access 9, 52883–52890 (2021)

    Google Scholar 

  18. Alblawi, A.S., Alhamed, A.A.: Big data and learning analytics in higher education: demystifying variety, acquisition, storage, NLP and analytics. In: 2017 IEEE Conference on Big Data and Analytics, pp. 124–129. IEEE (2017)

    Google Scholar 

  19. Dabbèchi, H., Haddar, N.Z., Elghazel, H., Haddar, K.: Nosql data lake: a big data source from social media. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Rios, T.N., Hong, T.-P. (eds.) Hybrid Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 1375, pp. 93–102. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-73050-5_10

    Chapter  Google Scholar 

  20. Solodovnikova, D., Niedrite, L.: Change discovery in heterogeneous data sources of a data warehouse. In: Robal, T., Haav, H.-M., Penjam, J., Matulevičius, R. (eds.) Databases and Information Systems. Communications in Computer and Information Science, vol. 1243, pp. 23–37. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57672-1_3

    Chapter  Google Scholar 

  21. Saddad, E., Mokhtar, H.M.O., El-Bastawissy, A., Hazman, M.: Lake data warehouse architecture for big data solutions. Int. J. Adv. Comput. Sci. Appl. 11(8), 417–424 (2020)

    Google Scholar 

  22. Ang, K.L.M., Ge, F.L., Seng, K.P.: Big educational data and analytics: survey, architecture and challenges. IEEE Access 8, 116392–116414 (2020)

    Article  Google Scholar 

  23. Khan, A., Ghosh, S.K.: Student performance analysis and prediction in classroom learning: a review of educational data mining studies. Educ. Inf. Technol. 26(1), 205–240 (2021). https://doi.org/10.1007/s10639-020-10230-3

    Article  Google Scholar 

  24. Sebaa, A., Chikh, F., Nouicer, A., Tari, A.: Medical big data warehouse: architecture and system design, a case study: improving healthcare resources distribution. J. Med. Syst. 42(4), 1–16 (2018). https://doi.org/10.1007/s10916-018-0894-9

    Article  Google Scholar 

  25. Ngo, V.M., Le-Khac, N.-A., Kechadi, M.-T.: Designing and implementing data warehouse for agricultural big data. In: Chen, K., Seshadri, S., Zhang, L.-J. (eds.) Big Data – BigData 2019. Lecture Notes in Computer Science, vol. 11514, pp. 1–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23551-2_1

    Chapter  Google Scholar 

  26. Sellami, A., Nabli, A., Gargouri, F.: Transformation of data warehouse schema to NoSQL graph data base. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds.) Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing, vol. 941, pp. 410–420. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-16660-1_41

    Chapter  Google Scholar 

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Correspondence to Lamya Oukhouya .

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Oukhouya, L., El haddadi, A., Er-raha, B., Asri, H., Laaz, N. (2023). A Proposed Big Data Architecture Using Data Lakes for Education Systems. In: Ben Ahmed, M., Abdelhakim, B.A., Ane, B.K., Rosiyadi, D. (eds) Emerging Trends in Intelligent Systems & Network Security. NISS 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-031-15191-0_6

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