Optimized-Memory Map-Reduce Algorithm for Mobile Learning

  • Mamo M. Husain
  • Hamid A. JalabEmail author
  • Vala Ali Rohani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)


The increasing accessibility of mobile technologies and devices, such as smartphones and tablet PCs, has made mobile learning (m-learning) a critical feature of modern didactics. Mobile learning is among the many computerized activities that can be performed using mobile devices. As the volume of accessible important information on university websites continues to increase, students may face difficulties in accessing important information from a large dataset. This study introduces an algorithmic framework for data reduction that is built on optimized-memory map–reduce algorithm for mobile learning. The goal of this method is to generate meaningful recommendations to a collection of students in the easiest and fastest way by using a recommender system. Through an experiment, the proposed method has demonstrated significant improvements in data size reduction up to 77 %. Such improvements are greater than those that are achieved using alternate methods.


Data reduction Mapreduce technique Mobile learning Content based recommendation 



The study is supported by Project No.: RG312-14AFR from University of Malaya.


  1. 1.
    Kukulska-Hulme, A.: Mobile usability in educational contexts: what have we learnt? Int. Rev. Res. Open Distrib. Learn. 8, 1–16 (2007)Google Scholar
  2. 2.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRefGoogle Scholar
  3. 3.
    Luo, T., Chen, G., Zhang, Y.: H-DB: yet another big data hybrid system of hadoop and DBMS. In: Kołodziej, J., Di Martino, B., Talia, D., Xiong, K. (eds.) ICA3PP 2013, Part I. LNCS, vol. 8285, pp. 324–335. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Liu, G.Z., Hwang, G.J.: A key step to understanding paradigm shifts in e-learning: towards context-aware ubiquitous learning. Br. J. Educ. Technol. 41, E1–E9 (2010)CrossRefGoogle Scholar
  5. 5.
    Wang, Y.S., Wu, M.C., Wang, H.Y.: Investigating the determinants and age and gender differences in the acceptance of mobile learning. Br. J. Educ. Technol. 40, 92–118 (2009)CrossRefGoogle Scholar
  6. 6.
    Peng, H., Su, Y.J., Chou, C., Tsai, C.C.: Ubiquitous knowledge construction: Mobile learning re-defined and a conceptual framework. Innov. Educ. Teach. Int. 46, 171–183 (2009)CrossRefGoogle Scholar
  7. 7.
    El-Hussein, M.O.M., Cronje, J.C.: Defining mobile learning in the higher education landscape. J. Educ. Technol. Soc. 13, 12–21 (2010)Google Scholar
  8. 8.
    Yang, X.Y., Liu, Z., Fu, Y.: MapReduce as a programming model for association rules algorithm on Hadoop. In: 2010 3rd International Conference on Information Sciences and Interaction Sciences (ICIS), pp. 99–102. IEEE (2010)Google Scholar
  9. 9.
  10. 10.
    Michigan University website.
  11. 11.
    Amazon books database.
  12. 12.
    Kumar, A., Kiran, M., Prathap, B.: Verification and validation of mapreduce program model for parallel k-means algorithm on hadoop cluster. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–8. IEEE (2013)Google Scholar
  13. 13.
    Moturi, C.A., Maiyo, S.K.: Use of mapreduce for data mining and data optimization on a web portal. Int. J. Comput. Appl. 56, 39–43 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mamo M. Husain
    • 1
  • Hamid A. Jalab
    • 1
    Email author
  • Vala Ali Rohani
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations