â¢The first stage shows the research of works related to Big Data in selected databases of scientific journals (Scopus, EBSCO, Springer Link and Science Direct).
Big Data in academic libraries: Literature review and future direction Hafidha Al-Barashdi The research council
Rahma Al-Karousi Rustaq college of Education
• Introduction The research framework Definitions and approaches to Big Data
Big Data analyzing techniques
Big Data tools
The benefits of Big Data in academic libraries
Big Data tools for batch analysis
Big Data tools for stream analysis
Big Data tools based on interactive analysis
Google MapReduce
Apache Storm
Apache Drill
Apache Hadoop
Apache S4
SpagoBI
Microsoft Dryad
Apache Spark
D3
Apache Mahout
MOA
How to involve librarian in Big Data?
Research Questions Q1
• What are the definitions and approaches to Big Data in academic libraries?
• What are the Big Data analyzing techniques and tools Q2 suitable for academic libraries?
Q3
• What are the benefits of Big Data in academic libraries?
Q4
• How to involve librarians in Big Data?
• What are the gaps in Big Data studies related to Q5 academic libraries and research trends of the future?
Research methodology The Literature review is composed of three stages:
The first stage shows the research of works related to Big Data in selected databases of scientific journals (Scopus, EBSCO, Springer Link and Science Direct). The second stage concerned the classification of these works in the different fields of knowledge according to the research questions. The third stage involved the report of detailed literature review.
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1
0 2010
2011
2012 Scopus
2013 EBSCO
Springer Link
2014 Science Direct
2015
2016
2017
Google scholar
Selected articles and databases according to publication year (total = 37)
• Rustles: Big Data definitions emerged from reviewing the literature: o These are information, technology, methods and impact. this study recommended using the definition “Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value”.
Big Data analyzing techniques are: statistics techniques. machine learning techniques. data mining techniques. signal processing techniques. visualization techniques.
This study recommended using these techniques in academic libraries.
Big Data tools are: Batch analysis. Stream analysis. Interactive analysis. This study recommended using these tools in academic libraries in order to benefit from Big Data opportunities.
The benefits of Big Data in academic libraries:
For management plans. Conceptual and theoretical understanding of Big Data and analytics within academic libraries. Supporting researchers. Investing in the opportunities of Big Data and text-miming methodology. This study recommended the academic libraries to benefit from the opportunities that the Big Data offers.
Involving librarian in Big Data: librarian roles in Big Data are: Realizing the capabilities of Big Data. Using Big Data tools. Depositing Big Data. Sharing Big Data services and products. Supporting Big Data management. Creating data literacy programs. Understanding the challenges associated with Big Data in academic libraries. This study recommended training the librarian regarding their new roles in Big Data services and products.
• Future research direction Future studies should survey the actual platforms or technologies used in library Big Data. Possible extensions to the present work include: A study of Big Data opportunities and challenges in academic libraries. A study of how Big Data is systematically affecting economic value in academic libraries. A proposal of guidelines for librarians on how to develop a system and process related to using Big Data in academic libraries. A study of how academic libraries can benefit from analyzing social media material for enhancing their information services.
