Opportunities and Implementation of Big Data Management in Academic Libraries

Author(s):  
Mahesh G. T. ◽  
Nandeesha B.

Data has changed the world in an unbelievable way and made an impact on our lifestyles at an exceptional rate. Big data is now the latest science of exploring and forecasting human-machine behavior dealing with a massive amount of associated data. The study is intended to understand the intensity and the competencies of librarians in implementing big data initiative project in academic libraries by the Government of Karnataka State. The study also tries to understand the application of big data in these libraries; 68 (87.17%) librarians completed the survey out of 78 respondents. The results of the study showed a strong association, that is, 72 (92.30%) respondents had the essential competencies and 58 (75.64%) librarians ability, intensity, readiness in implementing big data in academic libraries.

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.


Author(s):  
Honglong Xu ◽  
Haiwu Rong ◽  
Rui Mao ◽  
Guoliang Chen ◽  
Zhiguang Shan

Big data is profoundly changing the lifestyles of people around the world in an unprecedented way. Driven by the requirements of applications across many industries, research on big data has been growing. Methods to manage and analyze big data to extract valuable information are the key of big data research. Starting from the variety challenge of big data, this dissertation proposes a universal big data management and analysis framework based on metric space. In this framework, the Hilbert Index-based Outlier Detection (HIOD) algorithm is proposed. HIOD can handle all datatypes that can be abstracted to metric space and achieve higher detection speed. Experimental results indicate that HIOD can effectively overcome the variety challenge of big data and achieves a 2.02 speed up over iORCA on average and, in certain cases, up to 5.57. The distance calculation times are reduced by 47.57% on average and up to 89.10%.


2020 ◽  
Vol 37 (4) ◽  
pp. 1-5
Author(s):  
Nove E. Variant Anna ◽  
Endang Fitriyah Mannan

Purpose The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation. Design/methodology/approach The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019). Findings It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well. Originality/value This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.


Author(s):  
Md Rakibul Hoque ◽  
Yukun Bao

This chapter investigates the application, opportunities, challenges and techniques of Big Data in healthcare. The healthcare industry is one of the most important, largest, and fastest growing industries in the world. It has historically generated large amounts of data, “Big Data”, related to patient healthcare and well-being. Big Data can transform the healthcare industry by improving operational efficiencies, improve the quality of clinical trials, and optimize healthcare spending from patients to hospital systems. However, the health care sector lags far behind compared to other industries in leveraging their data assets to improve efficiencies and make more informed decisions. Big Data entails many new challenges regarding security, privacy, legal concerns, authenticity, complexity, accuracy, and consistency. While these challenges are complex, they are also addressable. The predominant ‘Big Data' Management technologies such as MapReduce, Hadoop, STORM, and others with similar combinations or extensions should be used for effective data management in healthcare industry.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adeyinka Tella ◽  
Kehinde Khadijat Kadri

Purpose The paper examined big data and academic libraries and emphasized whether it is big for something or nothing. Design/methodology/approach A conceptual and review analysis of documents was adopted to determine the concept of big data, the sources, the features, the relevance to academic libraries, specific case studies from around the world that have made use of big data, uses of big data in academic libraries, a review of best practices in the use of big data in academic libraries and the challenges. Findings The paper reports that although big data is indeed very big in academic libraries because there are evidences of its adoption and best practices in its use in academic libraries across the world, available challenges can render it big for nothing. Research limitations/implications This study is limited in terms of using literature review approach to discuss big data and academic libraries. The study is also limited in terms of focusing academic libraries and not taken other types of libraries into consideration. Practical implications The study has created awareness on the part of academic libraries stakeholders including authorities, librarians and users on the relevance of big data in academic and how big indeed it is in academic library landscape. The study also implied future related studies can borrow ideas from the current studies, which will inform whether an empirical evaluation is possible on the subject matter. Originality/value The paper is the original idea by the author, and it is to emphasize the relevance of big data in academic libraries and to prepare academic libraries that have not been tapping the opportunities of big data to get ready.


2020 ◽  
Vol 36 (3) ◽  
pp. 281-299
Author(s):  
Stefka Tzanova

In this paper we study the changes in academic library services inspired by the Open Science movement and especially the changes prompted from Open Data as a founding part of Open Science. We argue that academic libraries face the even bigger challenges for accommodating and providing support for Open Big Data composed from existing raw data sets and new massive sets generated from data driven research. Ensuring the veracity of Open Big Data is a complex problem dominated by data science. For academic libraries, that challenge triggers not only the expansion of traditional library services, but also leads to adoption of a set of new roles and responsibilities. That includes, but is not limited to development of the supporting models for Research Data Management, providing Data Management Plan assistance, expanding the qualifications of library personnel toward data science literacy, integration of the library services into research and educational process by taking part in research grants and many others. We outline several approaches taken by some academic libraries and by libraries at the City University of New York (CUNY) to meet necessities imposed by doing research and education with Open Big Data – from changes in libraries’ administrative structure, changes in personnel qualifications and duties, leading the interdisciplinary advisory groups, to active collaboration in principal projects.


2021 ◽  
Vol 7 (1) ◽  
pp. 14-17
Author(s):  
Ain Farhana Jamaludin ◽  
Muhammad Najib Razali ◽  
Rohaya Abdul Jalil ◽  
Siti Hajar Othman ◽  
Yasmin Mohd Adnan

Effective maintenance management requires proper data management for decision-making purposes. Big Data (BD) and Business Intelligence’s (BI) growing trend has created many challenges for government data management in particular. The government finds difficulties in integrating the massive volume of data with high-speed processing due to incapable database management in the current system, and the issues are not appropriately addressed. This paper contributes significantly, which focuses on an intelligent system that lets the government make an integral part of decision-making and can be applied horizontally to solve the problems in practice. Accordingly, an efficient data repository system with real-time analysis is proposed in this paper and it looks at a real case study highlighting the need for proper data management in government.


Jurnal Common ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 56-66
Author(s):  
Olih Solihin

This study aims to describe the implementation of big data on social media in carrying out crisis communication. This study uses a qualitative approach, with data collection techniques through a literature study. The results of this study also emphasize three aspects, namely: 1). The government must define a crisis because to understand a crisis, of course, we must start by defining it first. Each crisis that arises is accompanied by its own characteristics. 2). The government can use Big Data information sources to accelerate the implementation of government programs. 3). One of the efforts to look at big data to identify problems is to carry out the stages of social media. In the era of advances in communication and information technology, the existence of social media can no longer be separated from the activities of the world community..


Big Data ◽  
2016 ◽  
pp. 1189-1208 ◽  
Author(s):  
Md Rakibul Hoque ◽  
Yukun Bao

This chapter investigates the application, opportunities, challenges and techniques of Big Data in healthcare. The healthcare industry is one of the most important, largest, and fastest growing industries in the world. It has historically generated large amounts of data, “Big Data”, related to patient healthcare and well-being. Big Data can transform the healthcare industry by improving operational efficiencies, improve the quality of clinical trials, and optimize healthcare spending from patients to hospital systems. However, the health care sector lags far behind compared to other industries in leveraging their data assets to improve efficiencies and make more informed decisions. Big Data entails many new challenges regarding security, privacy, legal concerns, authenticity, complexity, accuracy, and consistency. While these challenges are complex, they are also addressable. The predominant ‘Big Data' Management technologies such as MapReduce, Hadoop, STORM, and others with similar combinations or extensions should be used for effective data management in healthcare industry.


2019 ◽  
Vol 34 (36) ◽  
pp. 1942025
Author(s):  
Robert D. Ryne

The first conference in what would become the International Computational Accelerator Physics (ICAP) series was held in 1988. At that time the most powerful computer in the world was a Cray YMP with 8 processors and a peak performance of 2 gigaflops. Today the fastest computer in the world has more than 2 million cores and a theoretical peak performance of nearly 200 petaflops. Compared to 1988, performance has increased by a factor of 100 million, accompanied by huge advances in memory, networking, big data management and analytics. By the time of the next ICAP in 2021 we will be at the dawn of the Exascale era. In this talk I will describe the advances in Computational Accelerator Physics that brought us to this point and describe trends in regard to large-scale accelerator simulation in the future.


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