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Big Data in Academic Libraries: Literature Review and Future Research Directions

المصدر: مجلة دراسات المعلومات والتكنولوجيا
الناشر: جمعية المكتبات المتخصصة فرع الخليج العربي ودار جامعة حمد بن خليفة للنشر
المؤلف الرئيسي: Al-Barashdi, Hafidha S. (Author)
مؤلفين آخرين: Al Karousi, Rahma (Co-Author)
المجلد/العدد: مج1, ع2
محكمة: نعم
الدولة: قطر
التاريخ الميلادي: 2018
الصفحات: 1 - 16
DOI: 10.5339/jist.2018.13
ISSN: 2616-4930
رقم MD: 1141601
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EduSearch, HumanIndex
مواضيع:
كلمات المؤلف المفتاحية:
Big Data | Big Data Tools | Big Data Analyzing Techniques | Academic Libraries
رابط المحتوى:
صورة الغلاف QR قانون

عدد مرات التحميل

37

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المستخلص: 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.

ISSN: 2616-4930

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