1887
Volume 2018, Issue 2
  • E-ISSN: 2616-4930

Abstract

Recently, Big Data studies have attracted considerable attention. However, Big Data analytics in academic libraries confront two fundamental challenges: the huge volume, velocity, and variety of data and the complexity of its techniques and algorithms. The primary aim of this study is to explore which techniques and tools can be applied in academic libraries in order to analyze Big Data, and then determine its profits in academic libraries. In addition, this study attempts to answer the following research questions: how should librarians be made to involve in Big Data? What are the future research developments in Big Data? What are the gaps in Big Data studies related to academic libraries? To provide a considerably better understanding of the advantages of Big Data in academic libraries and their future research directions, a comprehensive literature review of Big Data analysis of academic libraries over the last seven years was conducted. The results yielded a total of 37 papers related to Big Data in academic libraries. These results indicated that despite the large amount of research conducted on this topic, only a few studies discussed the implication of Big Data in academic libraries, including the analyzing tools and techniques. The benefits of Big Data in academic libraries and its implications on methodology in future studies are discussed. The present study also highlights the evolving field of Big Data research in academic libraries.

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2019-08-26
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References

  1. [1]. Akoka   J., , Comyn-wattiau   I., , Laou   N. . 2017; ; , Research on Big Data – A systematic mapping study. Computer Standards & Interfaces, 54(Part 2), 105–115. DOI: http://doi.org/10.1016/j.csi.2017.01.004 .
  2. [2]. Al-Daihani   S., , Abrahams   A. . 2016; ; , A text mining analysis of academic libraries' tweets. The Journal of Aca- demic Librarianship, 42, 135–143 .
  3. [3]. Andreu-Perez   J., , Poon   CC., , Merrifield   RD., , Wong   ST., , Yang   G-Z. . 2015; ; , Big Data for health. IEEE Journal of Biomedical and Health Informatics, 19(4), 1193–1208. DOI: 10.1109/JBHI.2015.2450362 .
  4. [4]. Archenaa   J., , Mary Anita   EA. . 2015; ; , A survey of Big Data analytics in healthcare and government. Procedia Computer Science, 50, 408–413 .
  5. [5]. Begoli   E., , Horey   J. . 2012; ; , Design principles for effective knowledge discovery from big data. In Joint ICSA and ECSA, 215–218. Available from: http://www.bdva.eu/sites/default/files/Design%20Principles%20for%20Effective%20Knowledge%20Discovery%20from%20Big%20Data.pdf .
  6. [6]. Beyer   MA., , Laney   D. . 2012; ; . The importance of ‘Big Data’: A definition. Stamford, CT: Gartner .
  7. [7]. Bizer   C., , Boncz   P., , Brodie   ML., , Erling   O. . 2011; ; , The meaningful use of Big Data: Four perspectives. SIGMOD, 40(4), 56–60 .
  8. [8]. Blascheck   T., , Burch   M., , Raschke   M., , Weiskopf   D. . 2015, September; ; . Challenges and perspectives in big eye- movement data visual analytics. In Big Data Visual Analytics (BDVA) (pp. 1–8). IEEE .
  9. [9]. Borin   J., , Yi   H. . 2008; ; , Indicators for collection evaluation: A new dimensional framework. Collection Building, 27(4), 136–143. DOI: http://dx.doi.org/10.1108/01604950810913698 .
  10. [10]. Borkar   V., , Carey   MJ., , Li   C. . 2012; ; . Inside “Big Data management”: Ogres, onions, or parfaits? In Proceeding of EDBT/ICDT joint conference. Berlin: ACM .
  11. [11]. Bostock   M., , Ogievetsky   V., , Heer   J. . 2011; ; , D3 data-driven documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301–2309 .
  12. [12]. Bu   Y., , Brokar   V., , Carey   MJ., , Rosen   J., , Polyzotis   N., , Condie   T., , … Ramakrishnan   R. . 2012; ; . Scaling datalog for machine learning on Big Data. Computer research repository (CoRR) (pp. 1–14). Cornell University Library. DOI: http://arxiv.org/pdf/1203.0160v2.pdf .
  13. [13]. Chen   H., , Chiang   RHL., , Storey   VC. . 2012; ; , Business intelligence and analytics: From Big Data to big impact. MISQ, 36(4), 1165–1188 .
  14. [14]. Chen   PCL., , Zhang   C-Y. . 2014; ; , Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347 .
  15. [15]. Cuzzocrea   A., , Song   IY., , Davis   K. . 2011; ; . Analytics over large-scale multidimensional data: The Big Data revo- lution! Proceedings of the 14th international workshop on Data Warehousing and OLAP (pp. 101–103). New York, NY: ACM .
  16. [16]. Daniel   B. . 2015; ; , Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. DOI: 10.1111/bjet.12230 .
  17. [17]. De Mauro   A., , Greco   M., , Grimaldi   M. . 2015, February; ; . What is Big Data? A consensual definition and a review of key research topics. In G. Giannakopoulos, D.P. Sakas, & D. Kyriaki-Manessi (Eds.), AIP Conference Proceedings (Vol. 1644, No. 1, pp. 97–104). Melville, NY: AIP Publishing .
  18. [18]. Dean   J., , Ghemawat   S. . 2008; ; , MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113 .
  19. [19]. Demchenko   Y., , Grosso   P., , de Laat   C., , Membrey   P. . 2013; ; , Addressing Big Data issues in scientific data infra- structure. In International conference on collaboration technologies and systems (CTS). IEEE Computer Society .
  20. [20]. Fan   W., , Bifet   A. . 2012; ; , Mining Big Data: Current status, and forecast to the future. SIGKDD Explorations, 14(2), 1–5 .
  21. [21]. Fisher   D., , DeLine   R., , Czerwinski   M., , Drucker   S. . 2012; ; , Interactions with Big Data analytics. Interactions, 19(3), 50–59 .
  22. [22]. Franceschini   M. . 2013; ; , How to maximize the value of Big Data with the open source SpagoBI suite through a comprehensive approach. Proceeding of the VLDB Endowment, 6(11), 1170–1171 .
  23. [23]. Gantz   J., , Reinsel   D. . 2011; ; , Extracting value from chaos. IDC iView, 1–12 .
  24. [24]. Girija   N., , Srivatsa   SK. . 2006; ; , A research study: Using data mining in knowledge base business strategies. Information Technology Journal, 5(3), 590–600. DOI: http://dx.doi.org/10.3923/itj.2006.590.600 .
  25. [25]. Goes   PB. . 2014; ; , Big Data and IS research methods. MIS Quarterly, 38(3), 3–8 .
  26. [26]. Gordon-Murnane   L. . 2012; ; , Big Data: A big opportunity for librarians. Online, 36(5), 30–34 .
  27. [27]. Han   J., , Kamber   M., , Pei   J. . 2011; ; . Data mining: Concepts and techniques (3rd ed.). Waltham, MA: Elsevier .
  28. [28]. Hashem   IAT., , Yaqoob   I., , Badrul Anuar   N., , Mokhtar   S., , Gani   A., , Khan   SU. . 2015; ; , The rise of “Big Data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115 .
  29. [29]. Heidorn   PB. . 2011; ; , The emerging role of libraries in data curation and e-science. Journal of Library Administration, 51(7–8), 662–672 .
  30. [30]. Herodotou   H., , Lim   H., , Luo   G. . 2011; ; . Starfish: A self-tuning system for Big Data analytics. In Proceeding of the 5th biennial conference on innovative data systems research (CIDR 11) (pp. 261–272) .
  31. [31]. Isard   M., , Budiu   M., , Yu   Y., , Birrell   A., , Fetterly   D. . 2007; ; . Dryad: Distributed data-parallel programs from sequential building blocks. In Proceeding of the 2ndACMSIGOPS/EuroSys European conference on com- puter systems (pp. 59–72) .
  32. [32]. Jacobs   A. . 2009; ; , The pathologies of Big Data. Communications of the ACM, 52(8), 36 .
  33. [33]. Jain   AK., , Murty   MN., , Flynn   PJ. . 1999; ; , Data clustering: A review. ACM Computing Surveys, 31(3), 264–323 .
  34. [34]. Keil   D. . 2014; ; , Research data needs from academic libraries: The perspective of a faculty researcher. Journal of Library Administration, 54(3), 233–240 .
  35. [35]. Khan   S., , Liu   X., , Shakil   KA., , Alam   M. . 2017; ; , A survey on scholarly data: From big data perspective. Informa- tion Processing & Management, 53(4), 923–944 .
  36. [36]. Kumar   P., , Priyadarsini   U. . 2016; ; , Revealing library statistics with Big Data expertise: A review. International Journal of Pharmacy & Technology, 8(4), 20783–20789. Available from: www.ijptonline.com .
  37. [37]. Laney   D. . 2001; ; , 3-D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70) .
  38. [38]. Larkou   G., , Mintzis   M., , Andreou   PG., , Konstantinidis   A., , Zeinalipour-yazti   D. . 2016; ; , Managing Big Data ex- periments on smartphones. Distributed and Parallel Databases, 34(1), 33–64. DOI: http://doi.org/10.1007/ s10619-014-7158-6 .
  39. [39]. Lomotey   RK., , Deters   R. . 2014; ; . Towards knowledge discovery in Big Data. In Proceeding of the 8th inter- national symposium on service oriented system engineering. IEEE Computer Society (pp. 181–191) .
  40. [40]. López   V., , del Río   S., , Benítez   JM., , Herrera   F. . 2014; ; , Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced Big Data. Fuzzy Sets and Systems, 258, 5–38 .
  41. [41]. Ma   C-L., , Shang   X-F., , Yuan   Y-B. . 2012; ; . A three-dimensional display for Big Data sets. In International confer- ence on machine learning and cybernetics (ICMLC) (pp. 1541–1545). IEEE Computer Society .
  42. [42]. Madden   S. . 2012; ; , From databases to Big Data. IEEE Internet Computing, 16(3), 4–6 .
  43. [43]. Manyika   J., , Chui   M., , Brown   B., , Bughin   J., , Dobbs   R., , Roxburgh   C., , Byers   AH. . 2011; ; . Big Data: The next fron- tier for innovation, competition and productivity. New York, NY: McKinsey Global Institute .
  44. [45]. Nabe   J. . 2011; ; , Changing the organization of collection development. Collection Management, 36, 3–16. DOI: http://dx.doi.org/10.1080/01462679.2011.529399 .
  45. [46]. Neumeyer   L., , Robbins   B., , Nair   A., , Kesari   A. . 2010; ; . S4: Distributed stream computing platform. In Proceeding of the 2010 international conference on data mining workshops (ICDMW). IEEE .
  46. [47]. Nicholson   S., , Stanton   J. . 2006; ; . Bibliomining for library decision-making. In Encyclopedia of data warehous- ing and mining (2nd ed., pp. 100–105). Available from: http://www.igi-global.com/chapter/encyclopedia- data-warehousing-mining/10591 .
  47. [48]. Owen   S., , Anil   R., , Dunning   T., , Friedman   E. . 2011; ; . Mahout in action. Greenwich, CT: Manning Publications .
  48. [49]. Prakash   K., , Chand   P., , Gohel   U. . 2004, November; ; . Application of data mining in library and information services. Paper presented at the 2nd Convention PLANNER, Manipur University, Imphal (pp. 168–177). Ahmedabad: INFLIBNET Centre. Available from: http://shodhganga.inflibnet.ac.in/dxml/handle/1944/435 .
  49. [50]. Rani   BR. . 2016, March 9–11; ; . Big Data and Academic Libraries. In International conference on Big Data and knowledge discovery. Indian Statistical Institute .
  50. [51]. Reinhalter   L., , Wittmann   RJ. . 2014; ; , The library: Big Data's boomtown. The Serials Librarian, 67(4), 363–372. DOI: 10.1080/0361526X.2014.915605 .
  51. [52]. Rodríguez-Mazahua   L., , Rodríguez-Enríquez   CA., , Sánchez-Cervantes   JL., , Cervantes   J., , García-Alcaraz   JL., , Alor-Hernández   G. . 2016; ; , A general perspective of Big Data: Applications, tools, challenges and trends. The Journal of Supercomputing, 72(8), 3073–3113 .
  52. [53]. Sagiroglu   S., , Sinanc   D. . 2013; ; . Big Data: A review. In IEEE international conference on CTS .
  53. [54]. Sandhu   G. . 2015, January 6–8; ; . Re-envisioning library and information services in the wake of emerging trends and technologies. The 4th international symposium on emerging trends and technologies in libraries and information services, Noida, India (pp. 153–160) .
  54. [55]. Schroeck   M., , Shockley   R., , Smart   J., , Romero-Morales   D., , Tufano   P. . 2012; ; , Analytics: The real-world use of Big Data. IBM Global Business Services, —Executive Report .
  55. [56]. Siguenza-Guzman   L., , Saquicela   V., , Avila-Ordóñez   E., , Vandewalle   J., , Cattrysse   D. . 2015; ; , Literature review of data mining applications in academic libraries. The Journal of Academic Librarianship, 41(4), 499–510 .
  56. [57]. Slavakis   K., , Giannakis   GB., , Mateos   G. . 2014; ; , Modeling and optimization for Big Data analytics. IEEE Signal Processing Magazine, 31(5), 18–31 .
  57. [58]. Stoica   I. . 2014, June; ; . Conquering Big Data with spark and BDAS. In Proceeding of the ACM international confer- ence on measurement and modeling of computer systems .
  58. [59]. Sumathi   S., , Sivanandam   SN. . 2006; ; . Introduction to data mining and its applications. Berlin: Springer .
  59. [60]. Suthaharan   S. . 2014; ; , Big Data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70–73. DOI:10.1145/2627534.2627557 .
  60. [61]. Van Weijen   D. . 2012; ; , The language of (future) scientific communication. Research Trends, 31, 7–8 .
  61. [62]. Wamba   S., , Akter   S., , Edwards   A., , Chopin   G., , Gnanzou   D. . 2015; ; , How ‘Big Data’ can make big impact: Find- ings from a systematic review and a longitudinal case study. International Journal of Production Econom- ics, 165, 234–246. DOI: 10.1016/j.ijpe.2014.12.031 .
  62. [63]. Ward   JS., , Barker   A. . 2013; ; , Undefined by data: A survey of Big Data definitions. Available from: http://arxiv.org/pdf/1309.5821v1.pdf .
  63. [64]. Wilkes   S. . 2012; ; , Some impacts of big data on usability practice. Communication Design Quarterly Review, 13(2), 25–32 .
  64. [65]. Witt   M. . 2012; ; , Co-designing, co-developing, and co-implementing an institutional repository service. Journal of Library Administration, 52(2), 172–188 .
  65. [66]. Wu   X., , Zhu   X., , Wu   G-Q., , Ding   W. . 2014; ; , Data mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107 .
  66. [67]. Yu   Y., , Isard   M., , Fetterly   D., , Budiu   M., , Erlingsson   Ú., , Gunda   PK., , Achan   K. . 2008; ; . DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. In Proceeding of the 8th USENIX conference on operating systems design and implementation (pp. 1–14) .
  67. [68]. Zhifeng   X., , Yang   X. . 2013; ; , Security and privacy in cloud computing. IEEE Communications Surveys and Tutor- ials, 15(2), 843–859 .
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  • Article Type: Review Article
Keyword(s): academic libraries , Big Data , Big Data analyzing techniques and Big Data tools
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