Big Data Management based on Cloud Computing in the Libraries of First Level Universities in Iran

Document Type : Original Article

Authors

• Seyedeh Elham Hashemi Bonjar ¹
• Soraya Ziaie ²
• Maryam Salami ³

¹ PhD Student of Knowledge & Information Science/ Payam Noor University, Tehran, Iran

² َAsociate Professor/ Department of Knowledge & Information Science, Payame Noor University, Tehran, Iran

³ Assistant Professor/ Department of Knowledge & Information Science, Payame Noor University, Tehran, Iran

10.22067/infosci.2021.67768.1001

Abstract

Introduction: Big data is a set of data that, with its special features, cannot be stored, managed and processed by conventional software systems. Cloud computing is a collection of virtualized resources with easy access and usability. Cloud-based storage technology is able to effectively manage big data. The purpose of this study is to determine the current status of libraries̓ big data management based on cloud computing and its Component ranking in the libraries of first level universities in Iran
Methodology: The present study is an applied and in terms of nature, method and control of variables is a descriptive research. The statistical population includes all librarians working in the libraries of first level universities in Iran, 520 people. The questionnaire was provided to the respondents electronically and a total of 393 questionnaires were received and analyzed. Data collection tool is a researcher-made questionnaire that examines previous studies of six main components affecting the libraries̓ big data management based on cloud computing in the libraries of first level universities in Iran that including manpower, organization, infrastructure, economic issues, culture and data management was identified, which was confirmed through confirmatory factor analysis. To analyze data from descriptive and inferential statistics with software Spss 22 and Amos 22 were used.
Findings: The current situation of library data management based on cloud computing in the libraries of Iran's top-level universities was considered unfavorable from the librarians' point of view, with an average of 23.582 and a standard deviation of 7.655 equal to -0.41730. Ranking the components of the current situation from strength to weakness, respectively 1. Manpower With a factor loading rate of 0.64, 2. Economic issues with a factor loading rate of0.60, 3. Infrastructure With a factor loading rate of0.59, 4. Data Management With a factor loading rate of0.58, 5. Culture With a factor loading rate of 0.57 6. Organizations With a factor loading rate of 0.52 are located.
Conclusion: Librarians are practically dealing with big data because of their day-to-day dealings with very large volumes of data. Matching the average budget of these libraries with the current needs and providing the necessary funds for the digitization of resources and the existence of appropriate legal hardware and software infrastructure, it can pave the way for the necessary steps to take advantage of cloud-based storage services. Most librarians, while understanding the culture of free access to information, understand the importance of providing analyzed data to clients, but clients' perception of receiving such data is moderate. Low awareness and understanding of officials and unwillingness to interact and cooperate with organizations active in this field, does not provide a stable environment to work with big data and provide services based on them.

Keywords

• Big Data Management
• Cloud Computing
• Academic Libraries
• Librarians

20.1001.1.22516344.1400.11.1.18.6

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References

آقایی میرک آباد، اعظم، آهنگر، عادله، آقایی میرک آباد، الهه و حسن‌پور، طلعت (1395). سیستم‌های مدیریت کتابخانه کلان دادهای مبتنی بر مدل ابری. به کوشش میترا قیاسی، سید علی اصغررضوی، صفیه طهماسبی لیمونی، مجموعه مقالات همایش ملی مدیریت داده‌های عظیم در علم اطلاعات و دانش‌شناسی (ص 67-78). بابل: دانشگاه آزاد اسلامی.

ارل، توماس، ختاک، وجید و بوهلر، پاول. (1395). اصول داده‌های بزرگ مفاهیم، پیشران‌ها و تکنیک‌ها؛ ترجمه رشیدی و دیگران. تهران: انتشارات آتی نگر.

اسفندیاری مقدم، علیرضا، حسن‌زاده، محمد و غیوری، زینب (1390). تحلیل عامل‌های اثرگذار بر تولیدات علمی زنان ایرانی در آی. اس. آی. پژوهشنامه کتابداری و اطلاع‌رسانی،1 (1)،109-134.

https://dx.doi.org/10.22067/riis.v1i1.8487

چن، مین، ماو، شیون، ژانگ، ین و ام.لیونگ، ویکتور سی (1394). کلان داده‌ها: فناوری‌ها؛ ترجمه امیر مسعود رحمانی، رقیه توکلیان. بابل: انتشارات علوم رایانه.

حبیبی، آرش (1396). مدل یابی معادلات ساختاری و تحلیل عاملی (آموزش کاربردی نرم‌افزار LISREL). تهران:  جهاد دانشگاهی.

درمنده، مژده، نوروزی، علیرضا و اسمعیلی گیوی، محمدرضا (1397). فرصت‌های مدیریت داده‌های بزرگ در کتابخانه‌ها و مراکز اطلاع‌رسانی: واکاوی ساختاری-تفسیری و ارائه راهکار. پژوهشنامه پردازش و مدیریت اطلاعات. ۱۳۹۷; ۳۴ (۲): ۸۴۱-۸۷. http://jipm.irandoc.ac.ir/article-1-3866-fa.html

ساسینسکی، بری ا.(1390). مرجع کامل رایانش ابری. ترجمه نوید فرخی. تهران: علوم رایانه.

سلیمان دهکردی، الهام؛ افراسیابی، علی (1394). حق دسترسی آزاد به اطلاعات در نظام حقوقی بین‌المللی و ملی. مطالعات بین‌المللی پلیس، 6(21)، 75-100. http://interpol.jrl.police.ir/article_12886.html

‎ شهرزادی، لیلا (1391). کارگاه آشنایی با منابع اطلاعاتی علمی الکترونیکی. دانشگاه علوم پزشکی اصفهان. بازیابی شده از: http://lib.mui.ac.ir/sites/lib.mui.ac.ir/files/amozesh/ashen.pdf

صراف‌زاده، مریم (1394). مدیریت کلان‌داده‌های پژوهشی: نقشی نوین برای کتابخانه‌های دانشگاهی. فصلنامه نقد کتاب اطلاع‌رسانی و ارتباطات. 6، 265 – 274. http://icbr.faslnameh.org/article-1-183-fa.html

قبادپور، وفا، نقشینه، نادر و ثابت پور، افسون (1391). از رایانش ابری تا کتابخانه ابری و ارائه پیشنهاد طراحی کتابخانه با الگوی رایانش ابری. پردازش و مدیریت اطلاعات (علوم و فناوری اطلاعات)، 28(4)،859-877.‎

http://jipm.irandoc.ac.ir/article-1-2384-fa.html

معین، محمد شهرام (1395). الزامات قانونی کلان داده‌ها. پروژه تدوین نقشه راه کلان داده‌ها. تهیه‌کننده فاطمه کسائی. تهران: مرکز تحقیقات مخابرات ایران.

Adebayo, O. A., Ahmed, Y. O., & Adeniran, R. T. (2018). The role of ict in provision of library services a panacea for sustainable development in Nigeria. Library Philosophy & Practice.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=5305&context=libphilprac (accessed 11 July 2019).

Ahmad, K., JianMing, Z., & Rafi, M. (2019). An analysis of academic librarians’ competencies and skills for implementation of Big Data analytics in libraries. Data Technologies and Applications. https://doi.org/10.1108/DTA-09-2018-0085

Al-Daihani, S. M., & Abrahams, A. (2016). A text mining analysis of academic libraries' tweets. The journal of academic librarianship, 42(2), 135-143.

https://doi.org/10.1016/j.acalib.2015.12.014

Avram, M. G. (2014). Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology, 12(0), 529-534.

https://doi.org/10.1016/j.protcy.2013.12.525

Ball, R. (2019). Big data and their impact on libraries. American Journal of Information Science and Technology, 3(1), 1-9.

http://dx.doi.org/10.11648/j.ajist.20190301.11

Cottrell, T., & Bell, B. (2016). When to say when: Using big data to support mobile communications. College & Undergraduate Libraries, 23(3), 315-322.

https://doi.org/10.1080/10691316.2015.1023878

Del Giudice, M., & Della Peruta, M. R. (2016). The impact of IT-based knowledge management systems on internal venturing and innovation: a structural equation modeling approach to corporate performance. Journal of Knowledge Management. 20)3(.

https://doi.org/10.1108/JKM-07-2015-0257

Fister, B. (2015). Big data or big brother? data, ethics, and academic libraries. Library Issues: Briefings for Faculty and Administrators, 35(4), 293-314.

Available at: http://barbarafister.com/LIbigdata.pdf (accessed 15 December 2019).

Gantz, J., & Reinsel, D. (2011). Extracting value from chaos. IDC rview, 1142(2011), 1-12.

http://www.kushima.org/wp-content/uploads/2013/05/DigitalUniverse2011.pdf (accessed 25 June 2018).

Gordon-Murnane, L. (2012). Big Data: A big opportunity for librarians. Online (Weston, CT), 36(5), 30-34.

http://eprints.rclis.org/34349/1/final-proceeding-iicclist16-2s-view-50-51.pdf (accessed 21 Apr 2019).

Hamad, F., Fakhuri, H., & Abdel Jabbar, S. (2020). Big Data Opportunities and Challenges for Analytics Strategies in Jordanian Academic Libraries. New Review of Academic Librarianship, (just-accepted), 1-19.

https://doi.org/10.1080/13614533.2020.1764071

Head, S. R., Komori, H. K., LaMere, S. A., Whisenant, T., Van Nieuwerburgh, F., Salomon, D. R., & Ordoukhanian, P. (2014). Library construction for next-generation sequencing: overviews and challenges. Biotechniques, 56(2), 61-77.

https://www.future-science.com/doi/full/10.2144/000114133 (accessed 3 Apr 2019).

Heidorn, P. B. (2011). The emerging role of libraries in data curation and e-science. Journal of Library Administration, 51(7-8), 662-672. https://doi.org/10.1080/01930826.2011.601269

Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big data: Issues and challenges moving forward. InSystem Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 995-1004). IEEE.

https://doi.org/10.1109/HICSS.2013.645

Keil, D. E. (2014). Research data needs from academic libraries: The perspective of a faculty researcher. Journal of Library Administration, 54(3), 233-240.

https://doi.org/10.1080/01930826.2014.915168

Kim, S., & Choi, M. S. (2016, February). Study on data center and data librarian role for reuse of research data. In 2016 8th International Conference on Knowledge and Smart Technology (KST) (pp. 303-308). IEEE. https://doi.org/10.1109/KST.2016.7440517

Kim, Y. S., & Cooke, L. (2017). Big data analysis of public library operations and services by using the Chernoff face method. Journal of Documentation.

https://doi.org/10.1108/JD-08-2016-0098

Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.

http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (accessed 8 may 2019).

Li, J., Lu, M., Dou, G., & Wang, S. (2017). Big data application framework and its feasibility analysis in library. Information Discovery and Delivery.

https://doi.org/10.1108/IDD-03-2017-0024

Lin, C. H., Lin, I. C., Roan, J. S., & Yeh, J. S. (2012). Critical factors influencing hospitals’ adoption of HL7 version 2 standards: An empirical investigation. Journal of medical systems, 36(3), 1183-1192. https://doi.org/10.1007/s10916-010-9580-2

Lyon, L., Wright, S., Corti, L., Edmunds, S., & Bennett, F. (2013). What is a data scientist? Panel presented at 2013 International Digital Curation Conference, January 14-17, 2013, Amsterdam, Netherlands.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., 2011. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

Marshall, J. G., Sollenberger, J., Easterby-Gannett, S., Morgan, L. K., Klem, M. L., Cavanaugh, S. K., … & Hunter, S. (2013). The value of library and information services in patient care: results of a multisite study. Journal of the Medical Library Association: JMLA, 101(1), 38. https://dx.doi.org/10.3163%2F1536-5050.101.1.007

Marzi, G., Dabić, M., Daim, T., & Garces, E. (2017). Product and process innovation in manufacturing firms: a 30-year bibliometric analysis. Scientometrics, 113(2), 673-704.

https://doi.org/10.1007/s11192-017-2500-1

Pryor, G., & Donnelly, M. (2009). Skilling up to do data: whose role, whose responsibility, whose career. International Journal of Digital Curation, 4(2), 158-170.

https://doi.org/10.2218/ijdc.v4i2.105

Raisch, S., Birkinshaw, J., Probst, G., & Tushman, M. L. (2009). Organizational ambidexterity: Balancing exploitation and exploration for sustained performance. Organization science, 20(4), 685-695. https://doi.org/10.1287/orsc.1090.0428

Rani, B. R. (2016). Big Data and Academic Libraries. In International conference on Big Data and knowledge discovery. Indian Statistical Institute.

Qutab, S., Bhatti, R., & Ullah, F. S. (2014). Adoption of ICT's for library operations and services: A comparison of public and private university libraries of Pakistan. Library Philosophy and Practice, 0_1.

https://digitalcommons.unl.edu/libphilprac/1106 (accessed 21 June 2017).

Reinhalter, L., & Wittmann, R. J. (2014). The Library: Big Data’s Boomtown: Edited by Rick J. Block. The Serials Librarian, 67(4), 363-372.

https://doi.org/10.1080/0361526X.2014.915605

Swan, A., & Brown, S. (2008). The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs. Truro: Key Perspectives Ltd. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.8960&rep=rep1&type=pdf

Teets, M., & Goldner, M. (2013). Libraries’ role in curating and exposing big data. Future internet, 5(3), 429-438. https://doi.org/10.3390/fi5030429

Witt, M. (2012). Co-designing, co-developing, and co-implementing an institutional data repository service. Journal of Library Administration, 52(2), 172-188.

https://doi.org/10.1080/01930826.2012.655607

Yuhong, X. I. E. (2018). Utilization and Reconstruction of Digital Resources in University Libraries in the Perspective of Big Data Based on the Statistics of Digital Resources Utilization in the Changbei University Alliance Library. Modern Information Technology, (6), 61.

https://en.cnki.com.cn/Article_en/CJFDTOTAL-XDXK201806061.htm