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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.

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).

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.

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.

Purpose. To offer a range of practical suggestions for library staff and others who intend to collect and analyze data that describe their networked services. Method/approach. Based on the author's experience in completing a number of funded research projects on this topic, working directly with libraries in collecting networked statistics and implementing the findings from those efforts, and observing best practices at those libraries. Findings. Factors dealing with understanding the evaluation context, planning, training, organization, determining appropriate data collection methods, managing data, and knowing the target audience for reporting are all‐important. Details a number of specific factors and strategies that can be used to increase the success of collecting networked statistics. Practical implications. Thinking about the preparation needed to collect networked statistics and how the process can be handled as efficiently as possible before the effort begins can help ensure that the library staff uses its time as wisely as possible in the data collection process, collects high quality and useful data, integrates these data into other library data, can draw upon the data for future purposes through a management information system, and uses the data to improve overall library decision making, planning, and meeting community information needs.

The purpose of this paper is to describe the need for academic libraries to demonstrate and increase their impact of student learning and success. It highlights the data problems present in existing library value correlation research and suggests a pathway to surmounting existing data obstacles. The paper advocates the integration of libraries into institutional learning analytics systems to gain access to more granular student learning and success data. It also suggests using library-infused learning analytics data to discover and act upon new linkages that may reveal library value in an institutional context.

The paper describes a pattern pervasive in existing academic library value correlation research and identifies major data obstacles to future research in this vein. The paper advocates learning analytics as one route to access more usable and revealing data. It also acknowledges several challenges to the suggested approach.

This paper describes learning analytics as it may apply to and support correlation research on academic library value. While this paper advocates exploring the integration of library data and institutional data via learning analytics initiatives, it also describes four challenges to this approach including librarian concerns related to the use of individual level data, the tension between claims of correlation and causation in library value research, the need to develop interoperability standards for library data and organizational readiness and learning analytics maturity issues.

This paper outlines a path forward for academic library value research that may otherwise be stymied by existing data difficulties.

Electronically stored data on libraries, librarians, library users, and the information industry that supports library collections can assist librarians in projecting trends for the future directions of the profession. The purpose of this paper is to direct librarians to a selected list of providers of this data and their publications, particularly those sources that aggregate data in systematic, accessible formats.

Web sites of the organizations that collect data on libraries were examined for data reports generated either from research projects or from periodic reports.

There is a wealth of free data from ongoing research available on the web. In addition, there are proprietary data sets that librarians who need that specialized data should consider acquiring.

LIS professionals require access to current and retrospective data to shape future research and inform decision making.

The article fills the need for an annotated directory to data sources specific to the needs of libraries and procurement information for those sources.

This paper aims to describe how linked data technologies can change the digital library collections, what are the benefits of linked data applications in digital libraries and what are the challenges of digital libraries in linked data environment.

The present study is based on substantial literature review on the applications of linked data technologies in digital libraries. The search engines such as Google, Yahoo and Google Scholar were used to find the relevant literature for the study. Online databases such as Pro Quest, Science Direct, Emerald and JSTORE were also used to find the relevant literature of the study. Databases of Library Sciences Library and Information Science and Technology Abstracts and Library Information Science Abstracts were also used to find the relevant literature of the study. Library, linked data technologies, Semantic Web, digital library and digital collections were the main keywords which were used to find the relevant literature for the study.

The evolution of linked data technologies and Semantic Web has changed the traditional role of the libraries. Traditional libraries are converting into digital libraries and digital libraries are in a struggle to publish their resources on the Web using XML-based metadata standards. It has made capable the digital collections to be viewed by machines on the Web just like human. On the emergence of linked data applications in digital libraries, Web visibility of the libraries has been enhanced to provide the opportunities for the users to find their required quality information of libraries round-the-clock on the Web. National Library of France, National Library of Spain, Europeana, Digital Public Library of Americana, Library of Congress and The British Library have taken the initiatives to publish their resources on the Web using linked data technologies.

This study present several key issues for policy makers, software developers, decision makers and library administrators about linked data technologies and its implementations in digital libraries. The present study may play its role to facilitate the users of the Web who are enthusiastically interested to exploit the quality and authentic library resources on the Web round-the-clock. Search engines will also achieve their longstanding goal to exploit the quality resources of the libraries for their Web users to make their Web appearance more credible and trustworthy.

Quality management, and its associated evidence‐based ideology, have influenced library management from at least the beginning of the 1990s. Both concepts emphasize that decisions have to be based on facts. Libraries have a long history of using statistics in data collection due to international agreements and standards. Although there is much data available in the libraries, perhaps this collected data have not been utilized as effectively as possible. This paper aims to discuss the possibilities of combining different types of statistical data in order to measure the efficiency of the work being done in libraries and what possible impact they can have in their parent organizations. It also aims to use the concept of data warehousing as a tool for joining different types of statistical data in the analysis.

This paper is a case study with statistical analysis of library data collected and a conceptual analysis of possibilities in the data warehousing of the library and library‐related data in order to improve its analysis.

The authors suggest that in order to improve the analysis of the library statistical data towards the actual effects the library has, for example, to the learning and research done within university, one must start to develop statistical data systems that enable the combination of different types of statistical data. This needs co‐operation between different vendors and government officials in defining the statistical data collected.

The paper is based on Finnish experiences and the data warehousing model should be tested empirically and developed further due to the challenges in the conformity of the data collected.

The paper presents ideas and possibilities to evolve the library statistical data analysis.

The paper presents ideas for national and international bodies in how to develop statistical data systems and their interoperability.

The paper promotes the idea of combining library and other statistical data collected in order to provide better tools for library management.

The purpose of this paper is to share the research processes and results of secondary analysis using GIS to map usage of a university library to contribute to ongoing efforts to help identify how library spaces are used to explain how university libraries can continue to evolve as teaching, learning, and shared communities of scholars. This paper details the use of ArcGIS to visualize where students are in the library in order to explain how this method can used by libraries to visualize the use of their facilities.

This research utilized secondary analysis of data collected during seating sweeps; through secondary analysis, data were analyzed and visualized in ArcGIS. The seating sweeps were conducted three times a day during a sample week, with researchers noting on maps of the library floor plan where students were sitting. Data were entered into an ArcGIS database file and mapped to display usage directly on the library map to improve stakeholders’ understanding of the ways students are using the library as a place.

Even though this project used consistent instruments and procedural instructions and trained observers, a combination of factors resulted in an incomplete data set, including the length of time between research design and data collection and lack of agreement about the use of map worksheets. It was still possible to make maps that depict heavier and lighter areas of use, present data to library stakeholders, and show what can be accomplished when data are collected on copies of the floor plan.

This research is limited by being a conducted in one university library, but the implications far outweigh the limitations. While bar and pie charts are effective at visualizing data, they do not provide a way to visualize where activities occur; maps provide multi-layered visualization, allowing libraries to visualize the same usage data as bar, pie, or other charts in addition to seeing where that usage occurs. The implications for librarianship include better understanding of how library spaces are used and the ability to use visually appealing maps to demonstrate the library’s use, value, and impact.

Mapping library statistics is an area that has been growing in the last decade, but practical examples of using GIS to map facility usage are few. This paper explains in detail how the mapping process works and how libraries of all types can adapt this method for their own usage assessments to more vividly depict the value and impact of the library facility as a place.

This paper aims to describe a data analysis methodology using data‐ and knowledge‐mining techniques focused on libraries. It concerns data analysis techniques in general, as well as ways in which they could be applied to library management. The ultimate purpose of this data process is to make the exported information useful to decision makers, so as to help them with decision making and strategy planning. This will lead to a more efficient organisation of the internal processing, and to improvement of the services offered in a library.

Methodologies based on knowledge and data mining are used to analyse the real data in one specific case study library (Library of Technical University of Crete, Greece) in order to describe the concept better. The results obtained concern the extraction of information about the inter‐relations of data and the definition of factors that can be used in library management and strategic planning. The scope of the paper is to show how data coming from libraries can be analysed to give useful results for decision‐makers, in order to improve the services they offer.

The paper provides a detailed list of all existing data resources in a library and describes step‐by‐step an analysis methodology based on processes of knowledge discovery and mining from given data. It refers to general principles that should be used for choosing the data to be processed and for defining the way the data should be combined and connected.

The research reported in this paper can be extended to define other new indicators regarding the quality of services offered to libraries by using a greater amount of data for analysis.

Changes should be made in the way of choosing data for analysis. The way of choosing data here is based on a methodology according to knowledge and data‐mining principles. A definition of new indicators about the quality of services in libraries should be derived from this methodology.

The new thinking in the paper is in the way librarians and decision‐makers in libraries have to use data. The paper shows a way of choosing data that will be able to produce useful conclusions after a well‐described analysis. The paper will be useful for librarians and library managers who want to plan strategies for improving the services they offer.

This study aims to find out the role of big data in the field of library and information science by considering the opportunities and challenges.

This study, which was conducted on a desk, examined the rise and significance of big data in the field of library and information science in Nigeria. Also, systematic literature review is done, exploring blogs and wikis to gather data on the use of big data.

This study emphasizes that as data is the primary component in the creation of information, professionals in the fields of library and information science must develop the knowledge and abilities necessary to support big data analysis. It was determined that offering and facilitating big data analysis would guarantee our place as a priceless and crucial resource in the digital workplace.

To the best of the authors’ knowledge, this study will be one of its kind to examine the role of big data in the field of library and information science profession. Furthermore, the results of the study will expose librarians and information professionals on the opportunities and challenges inherent in the use of big data technologies.

This paper aims to highlight the impact of big data on library services. This study highlights the required skills of librarians and the application of big data analytics.

An analysis of the literature was also used to identify the various applications implemented in library services across the globe.

This study’s findings reveal that the role of big data remained limited because of a lack of knowledge and skills. Big data’s significant challenges include inadequate technical support, untrained librarians and financial constraints to meet these requirements. This paper highlighted the challenges and remedial measures that can be taken while adopting this technology in library services.

This paper is significant for librarians, practitioners and stakeholders of the various organization who desire to implement this technology in their respective libraries.