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Article
Publication date: 22 February 2024

Ranjeet Kumar Singh

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The…

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Abstract

Purpose

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

Design/methodology/approach

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

Findings

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

Research limitations/implications

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

Practical implications

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

Originality/value

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

Details

Digital Library Perspectives, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5816

Keywords

Article
Publication date: 25 March 2024

Yusuf Ayodeji Ajani, Emmanuel Kolawole Adefila, Shuaib Agboola Olarongbe, Rexwhite Tega Enakrire and Nafisa Rabiu

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

Abstract

Purpose

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

Design/methodology/approach

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

Findings

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

Research limitations/implications

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

Originality/value

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

Details

Digital Library Perspectives, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5816

Keywords

Article
Publication date: 26 March 2024

Md. Nurul Islam, Guangwei Hu, Murtaza Ashiq and Shakil Ahmad

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of…

Abstract

Purpose

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

Design/methodology/approach

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

Findings

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

Originality/value

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 23 March 2023

Mohd Naz’ri Mahrin, Anusuyah Subbarao, Suriayati Chuprat and Nur Azaliah Abu Bakar

Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data…

Abstract

Purpose

Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data Applications have been made viable by cloud computing technologies due to the tremendous expansion of data. Disaster management is one of the areas where big data applications are rapidly being deployed. This study looks at how big data is being used in conjunction with cloud computing to increase disaster risk reduction (DRR). This paper aims to explore and review the existing framework for big data used in disaster management and to provide an insightful view of how cloud-based big data platform toward DRR is applied.

Design/methodology/approach

A systematic mapping study is conducted to answer four research questions with papers related to Big Data Analytics, cloud computing and disaster management ranging from the year 2013 to 2019. A total of 26 papers were finalised after going through five steps of systematic mapping.

Findings

Findings are based on each research question.

Research limitations/implications

A specific study on big data platforms on the application of disaster management, in general is still limited. The lack of study in this field is opened for further research sources.

Practical implications

In terms of technology, research in DRR leverage on existing big data platform is still lacking. In terms of data, many disaster data are available, but scientists still struggle to learn and listen to the data and take more proactive disaster preparedness.

Originality/value

This study shows that a very famous platform selected by researchers is central processing unit based processing, namely, Apache Hadoop. Apache Spark which uses memory processing requires a big capacity of memory, therefore this is less preferred in the world of research.

Details

Journal of Science and Technology Policy Management, vol. 14 no. 6
Type: Research Article
ISSN: 2053-4620

Keywords

Article
Publication date: 29 March 2024

Anil Kumar Goswami, Anamika Sinha, Meghna Goswami and Prashant Kumar

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers…

Abstract

Purpose

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

Design/methodology/approach

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

Findings

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

Research limitations/implications

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

Practical implications

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

Originality/value

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 29 February 2024

Atefeh Hemmati, Mani Zarei and Amir Masoud Rahmani

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of…

Abstract

Purpose

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

Design/methodology/approach

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

Findings

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

Research limitations/implications

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

Originality/value

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

Details

International Journal of Pervasive Computing and Communications, vol. 20 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 8 June 2022

Larissa Statsenko, Aparna Samaraweera, Javad Bakhshi and Nicholas Chileshe

Based on the systematic literature review, this paper aims to propose a framework of Construction 4.0 (C4.0) scenarios, identifying Industry 4.0 (I4.0) enabling technologies and…

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Abstract

Purpose

Based on the systematic literature review, this paper aims to propose a framework of Construction 4.0 (C4.0) scenarios, identifying Industry 4.0 (I4.0) enabling technologies and their applications in the construction industry. The paper reviews C4.0 trends and potential areas for development.

Design/methodology/approach

In this research, a systematic literature review (SLR) methodology has been applied, including bibliographic coupling analysis (BCA), co-citation network analysis of keywords, the content analysis with the visualisation of similarities (VOSviewer) software and aggregative thematic analysis (ATA). In total, 170 articles from the top 22 top construction journals in the Scopus database between 2013 and 2021 were analysed.

Findings

Six C4.0 scenarios of applications were identified. Out of nine I4.0 technology domains, Industrial Internet of Things (IIoT), Cloud Computing, Big Data and Analytics had the most references in C4.0 research, while applications of augmented/virtual reality, vertical and horizontal integration and autonomous robotics yet provide ample avenues for the future applied research. The C4.0 application scenarios include efficient energy usage, prefabricated construction, sustainability, safety and environmental management, indoor occupant comfort and efficient asset utilisation.

Originality/value

This research contributes to the body of knowledge by offering a framework of C4.0 scenarios revealing the status quo of research published in the top construction journals into I4.0 technology applications in the sector. The framework evaluates current C4.0 research trends and gaps in relation to nine I4.0 technology domains as compared with more advanced industry sectors and informs academic community, practitioners and strategic policymakers with interest in C4.0 trends.

Details

Construction Innovation , vol. 23 no. 5
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 27 October 2023

Pulkit Tiwari

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

Abstract

Purpose

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

Design/methodology/approach

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

Findings

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

Originality/value

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

Details

Management of Environmental Quality: An International Journal, vol. 35 no. 2
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 19 January 2024

Shiyi Wang, Abhijeet Ghadge and Emel Aktas

Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve…

Abstract

Purpose

Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).

Design/methodology/approach

A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.

Findings

The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.

Originality/value

The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully.

Details

Supply Chain Management: An International Journal, vol. 29 no. 2
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 26 March 2024

Xichen Chen, Alice Yan Chang-Richards, Florence Yean Yng Ling, Tak Wing Yiu, Antony Pelosi and Nan Yang

Despite extensive academic research related to digital technologies (DT), their integration into architecture, engineering and construction (AEC) projects lags in practice. This…

Abstract

Purpose

Despite extensive academic research related to digital technologies (DT), their integration into architecture, engineering and construction (AEC) projects lags in practice. This paper aims to discover DT deployment patterns and emerging trends in real-life AEC projects.

Design/methodology/approach

A case study methodology was adopted, including individual case analyses and comparative multiple-case analyses.

Findings

The results revealed the temporal distribution of DT in practical AEC projects, specific DT products/software, major project types integrated with digital solutions, DT application areas and project stages and associated project performance. Three distinct patterns in DT adoption have been observed, reflecting the evolution of DT applications, the progression from single to multiple DT integration and alignment with emerging industry requirements. The DT adoption behavior in the studied cases has been examined using the technology-organization-environment-human (TOE + H) framework. Further, eight emerging trend streams for future DT adoption were identified, with “leveraging the diverse features of certain mature DT” being a shared recognition of all studied companies.

Practical implications

This research offers actionable insights for AEC companies, facilitating the development of customized DT implementation roadmaps aligned with organizational needs. Policymakers, industry associations and DT suppliers may leverage these findings for informed decision-making, collaborative educational initiatives and product/service customization.

Originality/value

This research provides empirical evidence of applicable products/software, application areas and project performance. The examination of the TOE + H framework offers a holistic understanding of the collective influences on DT adoption. The identification of emerging trends addresses the evolving demands of the AEC industry in the digital era.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

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