1887
Volume 2019, Issue 1
  • E-ISSN: 2616-4930

Abstract

Recently, big data investment has become important for organizations, especially with the fast growth of data following the huge expansion in the usage of social media applications, and websites. Many organizations depend on extracting and reaching the needed reports and statistics. As the investments on big data and its storage have become major challenges for organizations, many technologies and methods have been developed to tackle those challenges.

One of such technologies is Hadoop, a framework that is used to divide big data into packages and distribute those packages through nodes to be processed, consuming less cost than the traditional storage and processing methods. Moreover, it allows an organization to store all its data, unlike the traditional methods of selecting and discarding some data.

In this study, researchers investigate the investment of Hadoop in managing the academic libraries’ big data, focusing on three Vs (velocity, variety, and volume).

The studied population is academic libraries in Jordanian universities, and the results show how Hadoop framework using the map/reduce technique can be used to manage the big data of such libraries.

Loading

Article metrics loading...

/content/journals/10.5339/jist.2019.3
2019-03-13
2019-06-17
Loading full text...

Full text loading...

/deliver/fulltext/jist/2019/1/jist.2019.3.html?itemId=/content/journals/10.5339/jist.2019.3&mimeType=html&fmt=ahah

References

  1. 1. Jach   T., , Magiera   E., , Froelich   W. . 2015; ; , Application of HADOOP to Store and Process Big Data Gathered from an Urban Water Distribution System. Procedia Engineering, 119, 1375-1380 .
  2. 2. Cheng   Y., , Chen   K., , Sun   H., , Zhang   Y., , Tao   F. . 2017; ; , Data and knowledge mining with Big Data towards smart production. Journal of Industrial Information Integration, 9, pp.1–3. DOI:http://dx.doi: 10.1016/j.jii.2017.08.001 .
  3. 3. Samiya   K., , Xiufeng   L., , Shakil   K., , Alam   M. . 2017; ; , A survey on scholarly Data: from Big Data perspective. Information Processing & Management, 53(4), pp.923–944. DOI: https://doi.org/10.1016/j.ipm.2017.03.006 .
  4. 4. Chen   CP., , Zhang   C. . 2014; ; , Data-intensive applications, challenges, techniques, and technologies: A survey on Big Data. Information Sciences, 275, 314–347. DOI: http://dx.doi.org/10.1016/j.ins.2014.01.015 .
  5. 5. Sledgianowski   D., , Gomaa   M., , Tan   C. . 2017; ; , Toward integration of Big Data, technology and information systems competencies into the accounting curriculum. Journal of Accounting Education, 38, pp.81–93 .
  6. 6. Fayyad   U., , Piatetsky-Shapiro   G., , Smyth   P. . 1996, August; ; . Knowledge discovery and data mining: towards a unifying framework, In KDD Proceedings (Vol. 96, pp. 82-88) .
  7. 7. Oussous   A., , Benjelloun   F., , Lahcen   A., , Belfkih   S. . 2017; ; , Big data technologies: A Survey. Journal of King Saud University-Computer and Information Sciences, 30(4), pp.431–448. DOI:https://doi.org/10.1016/j.jksuci.2017.06.001 .
  8. 8. Chen   M., , Mao   S., , Liu   Y. . 2014a; ; , Big Data: a survey. Mobile Networks and Applications, 19(2), pp.171–209 .
  9. 9. Najafabadi   MM., , Villanustre   F., , Khoshgoftaar   TM., , Seliya   N., , Wald   R., , Muharemagic   E. . 2015; ; , Deep learning applications and challenges in Big Data analytics. Journal of Big Data, 2(1).p.1 .
  10. 10. Khan   N., , Yaqoob   I., , Hashem   IAT., , Inayat   Z., , Mahmoud Ali   WK., , Alam   M., , Shiraz   M., , Gani   A. . 2014; ; , Big Data: survey, technologies, opportunities, and challenges. The Scientific World Journal. vol. 2014, Article ID 712826, 18 pages, 2014. DOI: https://doi.org/10.1155/2014/712826 .
  11. 11. Usha   D., , Aps   AJ. . 2014; ; , A survey of Big Data processing in perspective of Hadoop and MapReduce. International Journal of Current Engineering and Technology, 4(2), pp. 602–606 .
  12. 12. Maheswari   N., , Sivagami   M. . 2016; ; , Large-scale data analytics tools: apache hive, pig, and hbase. In Data Science and Big Data Computing (pp. 191-220). Springer, Cham .
  13. 13. Wang   L., , Tao   J., , Ranjan   R., , Marten   H., , Streit   A., , Chen   J., , Chen   D. . 2013; ; , G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Generation Computer Systems, 29(3), pp.739–750 .
  14. 14. Kune   R., , Konugurthi   PK., , Agarwal   A., , Chillarige   RR., , Buyya   R. . 2016; ; , The anatomy of Big Data computing. Software: Practice and Experience, 46(1), pp.79–105 .
  15. 15. White   T. . Hadoop: the definitive guide. San Francisco, CA: O'Reilly Media Inc   2012; .
  16. 16. Mall   NN., , Rana   S. . 2016; ; , Overview of big data and Hadoop. Imperial Journal of Interdisciplinary Research, 2(5) .
  17. 17. Ghoting   A., , Krishnamurthy   R., , Pednault   E., , Reinwald   B., , Sindhwani   V., , Tatikonda   S., , .. Vaithyanathan   S. . SystemML: Declarative machine learning on MapReduce. In 2011 IEEE 27th International Conference on Data Engineering (pp. 231-242). IEEE   2011; .
  18. 18. Wang   G., , Salles   MV., , Sowell   B., , Wang   X., , Cao   T., , Demers   A., , .. White   W. . 2010; ; , Behavioral simulations in mapreduce. Proceedings of the VLDB Endowment, 3(1-2), 952–963 .
  19. 19. Zhang   X., , Yang   LT., , Liu   C., , Chen   J. . 2014; ; , A scalable two-phase top-down specialization approach for Data anonymization using mapreduce on cloud. IEEE Transactions on Parallel and Distributed Systems, 25(2), 363–373 .
  20. 20. Big-Data for development facts and figures . 2014; ; , Retrieved October 5, 2017 from http://www.scidev.net/mena/Data/feature/Big-Data-for-development-facts-and-figures-AR.html .
  21. 22. Hadoop importance in handling big data . 2016; ; , Retrieved on October 9, 2017, from http://blog.BigDataweek.com/2016/08/01/hadoop-important-handling-Big-Data/ .
  22. 23. Compression tames Big Data on Hadoop . 2013; ; , Retrieved October 9, 2017, from https://www.slideshare.net/rainstor/big-dataanalyticsonhadoopinfographic .
  23. 24. What makes Hadoop special? . 2013; ; , Retrieved October 9, 2017, from https://hyperstage.net/2013/08/what-makes-hadoop-special/ .
  24. 25. Doulkeridis   C., , Nørvåg   K. . 2014; ; , A survey of large-scale analytical query processing in MapReduce. The VLDB Journal, 23(3), 355–380 .
  25. 26. Eldawy   A., , Mokbel   MF. . 2013; ; , A demonstration of spatial hadoop: An efficient mapreduce framework for spatial data. Proceedings of the VLDB Endowment, 6(12), 1230–1233 .
  26. 27. Li   F., , Ooi   BC., , Özsu   MT., , Wu   S. . 2014; ; , Distributed data management using MapReduce. ACM Computing Surveys (CSUR), 46(3), 31 .
  27. 28. Chavan   V., , Phursule   RN. . 2014; ; , Survey paper on big data. International Journal of computer Science and Information Technologies, 5(6), 7932–7939 .
http://instance.metastore.ingenta.com/content/journals/10.5339/jist.2019.3
Loading
/content/journals/10.5339/jist.2019.3
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Big data , data management , Hadoop , knowledge integration and libraries
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error