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Reseach Article

A Dashboard of an Education Data Portal using Big Data Solutions

by R. A. Mahmood, M. Z. Rashad, M. A. El-dosuky
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 19
Year of Publication: 2014
Authors: R. A. Mahmood, M. Z. Rashad, M. A. El-dosuky
10.5120/15825-3633

R. A. Mahmood, M. Z. Rashad, M. A. El-dosuky . A Dashboard of an Education Data Portal using Big Data Solutions. International Journal of Computer Applications. 90, 19 ( March 2014), 1-5. DOI=10.5120/15825-3633

@article{ 10.5120/15825-3633,
author = { R. A. Mahmood, M. Z. Rashad, M. A. El-dosuky },
title = { A Dashboard of an Education Data Portal using Big Data Solutions },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 19 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number19/15825-3633/ },
doi = { 10.5120/15825-3633 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:11:26.142053+05:30
%A R. A. Mahmood
%A M. Z. Rashad
%A M. A. El-dosuky
%T A Dashboard of an Education Data Portal using Big Data Solutions
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 19
%P 1-5
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An Educational Data Portal (EDP) play important role in teaching and learning as it contains useful resources. Every big educational institutions such as university shall build an EDP soon or later. The aim of this study is to utilize Big Data solutions in building a Dashboard for an Education Data Portal. The proposed EDP is envisioned to be a core tool for all students and learning agencies, providing support for many types of views and content/instructional resources to allow effective data-driven decision-making for students, teacher and the public, based on recent standards. It supports many features such as accessibility of data and content anywhere, scalability, extensibility of functionality, and extensibility of the technology architecture to support integration with the Shared Learning Infrastructure (SLI). The Data Dashboard is highly scalable and extensible architecture that will grow, if necessary, to meet the needs of students, and educators

References
  1. Rosnaini Mahmud, Mohd Arif Hj Ismail, Fadzilah Abdul Rahman, Nurzatulshima Kamarudin, Aisyatul Radhiah Ruslan, Teachers' Readiness in Utilizing Educational Portal Resources in Teaching and Learning, Procedia - Social and Behavioral Sciences, Volume 64, 9 November 2012, Pages 484-491
  2. Paul A. Harris, Jonathan A. Swafford, Terri L. Edwards, Minhua Zhang, Shraddha S. Nigavekar, Tonya R. Yarbrough, Lynda D. Lane, Tara Helmer, Laurie A. Lebo, Gail Mayo, Daniel R. Masys, Gordon R. Bernard, Jill M. Pulley, StarBRITE: The Vanderbilt University Biomedical Research Integration, Translation and Education portal, Journal of Biomedical Informatics, Volume 44, Issue 4, August 2011, Pages 655-662
  3. Jules J. Berman, Chapter 8 - Simple but Powerful Big Data Techniques, Principles of Big Data, Morgan Kaufmann, Boston, 2013, Pages 99-127, Principles of Big Data, ISBN 9780124045767, http://dx. doi. org/10. 1016/B978-0-12-404576-7. 00008-3.
  4. S. Fiore, A. D'Anca, C. Palazzo, I. Foster, D. N. Williams, G. Aloisio, Ophidia: Toward Big Data Analytics for eScience, Procedia Computer Science, Volume 18, 2013, Pages 2376-2385, ISSN 1877-0509, http://dx. doi. org/10. 1016/j. procs. 2013. 05. 409.
  5. Zachary Dixon, Joe Moxley, Everything is illuminated: What big data can tell us about teacher commentary, Assessing Writing, Volume 18, Issue 4, October 2013, Pages 241-256, ISSN 1075-2935, http://dx. doi. org/10. 1016/j. asw. 2013. 08. 002. (http://www. sciencedirect. com/science/article/pii/S1075293513000330)
  6. http://blogs. sas. com/content/sascom/2012/04/12/turning-big-data-volume-variety-and-velocity-into-value/
  7. Paul Westerman, Chapter 1 - What is Data Warehousing?, The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, San Francisco, 2001, Pages 1-30, Data Warehousing, ISBN 9781558606845, http://dx. doi. org/10. 1016/B978-155860684-5/50001-6. (http://www. sciencedirect. com/science/article/pii/B9781558606845500016)
  8. Dimitri Theodoratos, Timos Sellis, Designing data warehouses, Data & Knowledge Engineering, Volume 31, Issue 3, November 1999, Pages 279-301, ISSN 0169-023X, http://dx. doi. org/10. 1016/S0169-023X(99)00029-4. (http://www. sciencedirect. com/science/article/pii/S0169023X99000294)
  9. Gavin Powell, chapter 2 - Introducing data warehouse tuning, Oracle Data Warehouse Tuning for 10g, Digital Press, Burlington, 2006, Pages 31-47, Oracle Data Warehouse Tuning for 10g, ISBN 9781555583354, http://dx. doi. org/10. 1016/B978-155558335-4/50003-8. (http://www. sciencedirect. com/science/article/pii/B9781555583354500038)
  10. Shaker H. Ali El-Sappagh, Abdeltawab M. Ahmed Hendawi, Ali Hamed El Bastawissy, A proposed model for data warehouse ETL processes, Journal of King Saud University - Computer and Information Sciences, Volume 23, Issue 2, July 2011, Pages 91-104, ISSN 1319-1578, http://dx. doi. org/10. 1016/j. jksuci. 2011. 05. 005. (http://www. sciencedirect. com/science/article/pii/S131915781100019X)
  11. Jiong Xie, FanJun Meng, HaiLong Wang, HongFang Pan, JinHong Cheng, Xiao Qin, Research on Scheduling Scheme for Hadoop Clusters, Procedia Computer Science, Volume 18, 2013, Pages 2468-2471, ISSN 1877-0509, http://dx. doi. org/10. 1016/j. procs. 2013. 05. 423. (http://www. sciencedirect. com/science/article/pii/S1877050913005668)
  12. Wagner Kolberg, Pedro de B. Marcos, Julio C. S. Anjos, Alexandre K. S. Miyazaki, Claudio R. Geyer, Luciana B. Arantes, MRSG – A MapReduce simulator over SimGrid, Parallel Computing, Volume 39, Issues 4–5, April–May 2013, Pages 233-244, ISSN 0167-8191, http://dx. doi. org/10. 1016/j. parco. 2013. 02. 001. (http://www. sciencedirect. com/science/article/pii/S0167819113000215)
  13. Minghong Lin, Li Zhang, Adam Wierman, Jian Tan, Joint optimization of overlapping phases in MapReduce, Performance Evaluation, Volume 70, Issue 10, October 2013, Pages 720-735, ISSN 0166-5316, http://dx. doi. org/10. 1016/j. peva. 2013. 08. 013. (http://www. sciencedirect. com/science/article/pii/S0166531613000916)
  14. Faraz Ahmad, Seyong Lee, Mithuna Thottethodi, T. N. Vijaykumar, MapReduce with communication overlap (MaRCO), Journal of Parallel and Distributed Computing, Volume 73, Issue 5, May 2013, Pages 608-620, ISSN 0743-7315, http://dx. doi. org/10. 1016/j. jpdc. 2012. 12. 012. (http://www. sciencedirect. com/science/article/pii/S0743731512002936)
  15. Jiong Xie, Yun Tian, Shu Yin, Ji Zhang, Xiaojun Ruan, Xiao Qin, Adaptive Preshuffling in Hadoop Clusters, Procedia Computer Science, Volume 18, 2013, Pages 2458-467, ISSN 1877-0509, http://dx. doi. org/10. 1016/j. procs. 2013. 05. 422. (http://www. sciencedirect. com/science/article/pii/S1877050913005656)
  16. http://www. cbtnuggets. com/it-training-videos/course/cbtn_hadoop/10621
  17. Judith Hurwitz, Alan Nugent, Dr. Fern Halper, and Marcia Kaufman , " Big Data For Dummies " , Wiley and sons Inc, 2013
  18. Paul C. Zikopoulos , Chris Eaton , Dirk deRoos , Thomas Deutsch , George Lapis, " Understanding Big Data ", IBM, 2011
  19. http://usny. nysed. gov/rttt/rfp/ds-07/
  20. M. A. El-Dosuky, Ahmed EL-Bassiouny, Taher Hamza, Magdy Rashad, ,"Food Recommendation Using Ontology and Heuristics", A. Ell Hassanien et al. (Eds. ): AMLTA 2012, CCIS 322, pp. 423–429, 2012. © Springer-Verlag Berlin Heidelberg 2012.
  21. M. A. El-Dosuky, M. Z. Rashad, T. T. Hamza, A. H. EL-Bassiouny, Robopinion: Opinion Mining Framework Inspired by Autonomous Robot Navigation, CoRR cs. CL/arXiv:1209. 0249: (2012).
Index Terms

Computer Science
Information Sciences

Keywords

Big Data MapReduce Hadoop Educational Data Portal