Volume 9, Issue 2 (9-2022)
Human Information Interaction 2022, 9(2): 12-24 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
khademizadeh S, mohammadi Z. A Systematic Review of Data Mining Applications in Digital Libraries. Human Information Interaction 2022; 9 (2)
URL: http://hii.khu.ac.ir/article-1-3015-en.html
URL: http://hii.khu.ac.ir/article-1-3015-en.html
Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract: (2796 Views)
Purpose: Study aimed to identify the applications of data mining in the provision of services, collection and management of digital libraries.
Methodology: This is an applied study in terms of purpose and in terms of method is qualitative research that have been done by systematic review method. For this purpose, articles have been obtained by searching databases of Springer, Emerald, ProQuest, Web of Science, Google Scholar, Science Direct, and Semantic Scholar.
Articles published between 2000 and 2021 have been scrutinized. The systematic review model of Kitchenham and Charter (2007) was surveyed. According to the inclusion criteria, 1296 articles have been extracted after initial refinement, and among them, 77 articles related to the subject have been identified by reviewing the titles of articles and entered the final review by reviewing the full text. In conclusion, 29 articles were chosen for final analysis. The Qualitative content- coding method was used for data analysis and qualitative analysis was performed by two coders. The agreement of the evaluators based on the formula of Miles and Haberman for the performed analyzes, 78.5 was obtained.
Findings: Based on the results of qualitative analysis, 74 basic, 13 organizing and 3 comprehensive themes of "digital services,” “digital library management" and "digital collection" have been identified, which in total define the application of data mining in digital libraries represented.
Conclusion: Using data mining techniques in digital libraries, a variety of information can be stored seamlessly in different classes so that the end user of the information could meet their information needs in the shortest possible time. On the other hand, libraries can provide more useful resources by analyzing their users' information interests, and this can be considered a turning point in situations where libraries are facing financial difficulties.
Methodology: This is an applied study in terms of purpose and in terms of method is qualitative research that have been done by systematic review method. For this purpose, articles have been obtained by searching databases of Springer, Emerald, ProQuest, Web of Science, Google Scholar, Science Direct, and Semantic Scholar.
Articles published between 2000 and 2021 have been scrutinized. The systematic review model of Kitchenham and Charter (2007) was surveyed. According to the inclusion criteria, 1296 articles have been extracted after initial refinement, and among them, 77 articles related to the subject have been identified by reviewing the titles of articles and entered the final review by reviewing the full text. In conclusion, 29 articles were chosen for final analysis. The Qualitative content- coding method was used for data analysis and qualitative analysis was performed by two coders. The agreement of the evaluators based on the formula of Miles and Haberman for the performed analyzes, 78.5 was obtained.
Findings: Based on the results of qualitative analysis, 74 basic, 13 organizing and 3 comprehensive themes of "digital services,” “digital library management" and "digital collection" have been identified, which in total define the application of data mining in digital libraries represented.
Conclusion: Using data mining techniques in digital libraries, a variety of information can be stored seamlessly in different classes so that the end user of the information could meet their information needs in the shortest possible time. On the other hand, libraries can provide more useful resources by analyzing their users' information interests, and this can be considered a turning point in situations where libraries are facing financial difficulties.
Keywords: Data Mining, Digital Libraries, Systematic Review, Digital Services, Digital Management, Digital Collection
References
1. [1]. Abazari, Z., & Haddadi, T. (2017). The role of data mining on the organizational performance of library managers in Teh-ran public universities with a knowledge management approach. Quarterly Journal of Knowledge Retrieval and Semantic Systems, 3 (12), 1-28. (In Persian)
2. [2]. Ahmad, M., & Abawajy, J. H. (2014). Dig-ital library service quality assessment model. Procedia Soc Behav Sci ,129; 571-80 [DOI:10.1016/j.sbspro.2014.03.715]
3. [3]. Almaghrabi, M. A., & Chetty. G. (2017). A Novel Data Mining Testbed for User Cen-tred Modelling and Personalisation of Dig-ital Library Services. In 2017 IEEE 13th International Conference on e-Science (e-Science), 434-435. IEEE. [DOI:10.1109/eScience.2017.58]
4. [4]. Ammari, M., & Chiadmi, D. (2012). De-sign of an Integrated Digital Library Sys-tem Based on Peer-to-Peer Data Mining. Int. J. Cyber Ethics Educ, 2; 1-14. [DOI:10.4018/ijcee.2012070101]
5. [5]. Baruque, C. B., Baruque, L. B., & Melo. R. N. (2006). Using data mining for the re-fresh of learning objects digital libraries. WSEAS Transactions on Computers, 5(11); 2662-2667.
6. [6]. Berendt, B., Krause, B., & Kolbe-Nusser, S. (2010). Intelligent scientific authoring tools: Interactive data mining for con-structive uses of citation networks. Infor-mation processing & management, 46(1); 1-10. [DOI:10.1016/j.ipm.2009.08.002]
7. [7]. Ceci, M., Loglisci, C., & Macchia, L. (2014). Ranking sentences for keyphrase extraction: a relational data mining ap-proach. Procedia Computer Science, 38; 52-59. [DOI:10.1016/j.procs.2014.10.011]
8. [8]. Chang, C., & Chen, R. (2006). Using data mining technology to solve classification problems: A case study of campus digital library. Electron. Libr, 24; 307-321. [DOI:10.1108/02640470610671178]
9. [9]. Chen, C., & Chen, A. (2007). Using data mining technology to provide a recom-mendation service in the digital library. Electron. Libr, 25; 711-724. [DOI:10.1108/02640470710837137]
10. [10]. Díaz-Valenzuela, I., Martin-Bautista, M. J. Vila, M. A., & Campaña, J. R. (2013). An automatic system for identifying authorities in digital libraries. Expert Systems with Appli-cations, 40(10); 3994-4002. [DOI:10.1016/j.eswa.2013.01.010]
11. [11]. Digital Library Federation. (1999). "A working definition of digital library". Re-trieved November, 2, 2008, from http://www.diglib.org/about/dldefinition.htm
12. [12]. Fox, R. (2010). Mining the digital library. OCLC Systems & Services: International digi-tal library perspectives [DOI:10.1108/10650751011087585]
13. [13]. Grant, M. J., & Booth, A. (2009). A typol-ogy of reviews: an analysis of 14 review types and associated methodologies. Health infor-mation & libraries journal, 26(2); 91-108. [DOI:10.1111/j.1471-1842.2009.00848.x] [PMID]
14. [14]. Kitchenham, B. & Charters S. (2007). Guidelines for performing systematic litera-ture reviews in software engineering. Keele University, University of Durham, School of Computer Science and Mathematics, De-partment of Computer Science. Keele, Durham: EBSE technical report.
15. [15]. Kong, J. (2021). Research on Personalized Information Service of University Library Based on Association Rules Min-ing. CONVERTER, 2021(6), 733-739.
16. [16]. Kovačević, A., Devedzic, V., & Pocajt, V. (2010). Using data mining to improve digital library services. Electron. Libr, 28; 829-843. [DOI:10.1108/02640471011093525]
17. [17]. Kumar, A., Saini, D., & Kumar, P. (2021). Use of K-Means Clustering Method for Books Data in Acharya Raghuveer Library, Central University of Himachal Pradesh, Dharamsha-la, India.
18. [18]. Lee, J. Y., Kim, H. & Kim, P. J. (2010). Domain analysis with text mining: Analysis of digital library research trends using profiling methods. Journal of Information Science, 36(2); 144-161. [DOI:10.1177/0165551509353251]
19. [19]. Lone, T. A., & Khan, R. A. (2014). Data Mining: Competitive Tool to Digital Library. DESIDOC Journal of Library & Information Technology, 34 (5); 401-406. [DOI:10.14429/djlit.34.6722]
20. [20]. Mishra, R. N. (2007). Implications of Data Mining in Digital Library Environment.
21. [21]. Mishra, R. N., & Mishra, A. (2013). Rele-vance of data mining in digital library. Inter-national Journal of Future Computer and Communication, 2(1); 10.-14 [DOI:10.7763/IJFCC.2013.V2.110]
22. [22]. Nicholson, S. (2003). Bibliomining for au-tomated collection development in a digital library setting: Using data mining to discover Web‐based scholarly research works. Journal of the American Society for information sci-ence and technology, 54(12); 1081-1090. [DOI:10.1002/asi.10313]
23. [23]. Nicholson, S. (2006). The basis for biblio-mining: Frameworks for bringing together us-age-based data mining and bibliometrics through data warehousing in digital library services. Information processing & manage-ment, 42(3); 785-804. [DOI:10.1016/j.ipm.2005.05.008]
24. [24]. Niqresh, M. (2021). The Influence of Data Mining in Increasing Benefits of Libraries in Jordanian Governmental Universities. Library Philosophy and Practice, 1-13.
25. [25]. Nivedhitha, G., & Rupavathy, N. (2018). Data mining in personalized service of digital library, 7; 51-53. [DOI:10.14419/ijet.v7i1.7.9570]
26. [26]. Pang, N., & Yan, F. (2012). The research on personalized service of digital library based on data mining. In 2012 National Conference on Information Technology and Computer Science, 871-874 Atlantis Press. [DOI:10.2991/citcs.2012.221]
27. [27]. Peng, N., & Yin, F. (2012). A study on digi-tal library personalization services based on data mining. Translated by Somayeh Panahi 2017. Librarian 2.0 3 (1). (In Persian)
28. [28]. Rahimi, A., Soleimani, M., & Hashemian, A. (2018). Evaluating the quality of digital li-brary services of Isfahan University of Medi-cal Sciences from the perspective of users us-ing the model. DigiQUAL Health Information Management, 15 (1); 46 -49. (In Persian)
29. [29]. Rattan, P. (2019). Data mining: A library utility model. European Journal of Research, 39; 45.
30. [30]. Schwartz, C. (2000). Digital libraries: An overview. Journal of Academic Librarianship, 26(6); 385-394. [DOI:10.1016/S0099-1333(00)00159-2]
31. [31]. Sharma, M. (2014). Data mining: A litera-ture survey. International Journal of Emerging Research in Management & Technology, 3(2).
32. [32]. Shu, Z. Y. (2010). The research of multi-media data mining in digital library. In The 2nd International Conference on Information Science and Engineering, 5504-5507. IEEE [DOI:10.1109/ICISE.2010.5690870]
33. [33]. Siguenza-Guzman, L., Saquicela, V., Avi-la-Ordóñez, E., Vandewalle, J., & Cattrysse, D. (2015). Literature review of data mining ap-plications in academic libraries. The Journal of Academic Librarianship, 41(4), 499-510. [DOI:10.1016/j.acalib.2015.06.007]
34. [34]. Song, Y., & Wei, R. (2011). Research on application of data mining based on FP-growth algorithm for digital library. In 2011 Second International Conference on Mechan-ic Automation and Control Engineering (pp. 1525-1528). IEEE. [DOI:10.1109/MACE.2011.5987239]
35. [35]. Sun, X., Kaur,J., Possamai, L., & Menczer, F. (2013). Ambiguous author query detection using crowdsourced digital library annota-tions. Information Processing & Manage-ment, 49(2); 454-464. [DOI:10.1016/j.ipm.2012.09.001]
36. [36]. Tramullas J, Sanchez-Casabon, A. L., & Garrido-Picazo, P. (2013). An evaluation based on the digital library user: An experi-ence with. [DOI:10.1016/j.sbspro.2013.02.037]
37. [37]. Xie, H., Li, X., Wang, T., Chen, L., Li, K., Wang, F. L., ... & Min, H. (2016). Personalized search for social media via dominating verbal context. Neurocomputing, 172, 27-37. [DOI:10.1016/j.neucom.2014.12.109]
38. [38]. Zhang, M. (2011). Application of Data Mining Technology in Digital Library. JCP, 6(4); 761-768. [DOI:10.4304/jcp.6.4.761-768]
39. [39]. Zhang, X., Yang, G., Li, X., & Li, J. (2011). Characteristic Practice in the Construction of the Chinese Medical Digital Library-Wanfang MED ONLINE as the Example of the Charac-teristic Resources Organization and Presenta-tion as Well as Data Mining of the Medical Literature. In International Conference on Asian Digital Libraries, 383-384. Springer, Berlin, Heidelberg.. [DOI:10.1007/978-3-642-24826-9_51]
40. [40]. Zhao, Y., Niu, Z., & Dai, L. (2010). Evalu-ation algorithm about digital library collec-tions based on data mining technology. In In-ternational Conference on Asian Digital Li-braries, 266-267 Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-13654-2_37]
41. [41]. Zhao, Y., Niu, Z., Peng, X., & Dai, L. (2011). A discretization algorithm of numeri-cal attributes for digital library evaluation based on data mining technology. In Interna-tional Conference on Asian Digital Libraries, 70-76. Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-24826-9_12]
42. [42]. Zhao, Y., Niu,Z., & Peng,X. (2014). Re-search on Data Mining Technologies for Complicated Attributes Relationship in Digital Library Collections. Applied Mathematics & Information Sciences, 8; 1173-1178. [DOI:10.12785/amis/080329]
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
