Big data application framework and its feasibility analysis in library

Date20 November 2017
DOIhttps://doi.org/10.1108/IDD-03-2017-0024
Pages161-168
Published date20 November 2017
AuthorJun Li,Ming Lu,Guowei Dou,Shanyong Wang
Subject MatterLibrary & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia
BIG DATA APPLICATIONS IN DIGITAL INFORMATION
SUPPLY CHAIN
Big data application framework and its
feasibility analysis in library
Jun Li
School of Management, University of Science and Technology of China, Hefei, China
Ming Lu
School of Finance and Public administration, Jiang Xi University of Finance and Economics, Nanchang, China
Guowei Dou
Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University,
Shenzhen, China, and
Shanyong Wang
School of Management, University of Science and Technology of China, Hefei, China
Abstract
Purpose – The purpose of this study is to introduce the concept of big data and provide a comprehensive overview to readers to understand big
data application framework in libraries.
Design/methodology/approach – The authors first used the text analysis and inductive analysis method to understand the concept of big data,
summarize the challenges and opportunities of applying big data in libraries and further propose the big data application framework in libraries. Then they
used questionnaire survey method to collect data from librarians to assess the feasibility of applying big data application framework in libraries.
Findings – The challenges of applying big data in libraries mainly include data accuracy, data reduction and compression, data confidentiality and
security and big data processing system and technology. The opportunities of applying big data in libraries mainly include enrich the library database,
enhance the skills of librarians, promote interlibrary loan service and provide personalized knowledge service. Big data application framework in
libraries can be considered from five dimensions: human resource, literature resource, technology support, service innovation and infrastructure
construction. Most libraries think that the big data application framework is feasible and tend to apply big data application framework. The main
obstacles to prevent them from applying big data application framework is the human resource and information technology level.
Originality/value – This research offers several implications and practical solutions for libraries to apply big data application framework.
Keywords Library, Big data, Big data application framework, Challenges and opportunities, Feasibility analysis,
Personalized information push service
Paper type Research paper
1. Introduction
With the rapid development of digital information techniques,
internet technology, internet of things and cloud computing,
data have increased in an unprecedented scale in different
fields (Alotaibi and Abdullah, 2016). According to a report
from McKinsey, a famous consultancy in the world, the total
data volume in the world will up to 15-20 ZB in 2020 and this
volume will double every two years in the future (James et al.,
2011). There is no doubt that we live in an era of data
abundance. These abundant data come from different
resources, such as sensors, smart devices, social networking
interactions, web pages, videos, images, personal browsing
history and online transaction and so on (Wu et al., 2014;
Alotaibi and Abdullah, 2016). Meanwhile, the abundant data
also present in different formats, such as structured data,
semi-structured data and unstructured data (Bello-Orgaz
et al., 2016;Zaharia et al., 2016).
With the explosive increase of data, there is a specific term
to describe these data, namely, big data (Alotaibi and
Abdullah, 2016). Since the early twenty-first century, the
research on big data has drawn great attention and increased
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
45/4 (2017) 161–168
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-03-2017-0024]
Received 30 March 2017
Revised 31 May 2017
Accepted 5 June 2017
161

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