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Book part
Publication date: 12 July 2021

Ryan Cheah Wei Jie, Cha Yao Tan, Fang Yenn Teo, Boon Hoe Goh and Yau Seng Mah

Big data have rapidly developed as a viable solution to many problems faced in engineering industries. Specifically, in the industry of water resource engineering, where…

Abstract

Big data have rapidly developed as a viable solution to many problems faced in engineering industries. Specifically, in the industry of water resource engineering, where there is a tremendous amount of data, various big data techniques could be applied to achieve innovative and efficient solutions for the industry. This study reviewed the proposal of big data as potential approaches to solve various difficulties encountered in managing water resources and related applications in Malaysia. The advantages and disadvantages of big data applications have also been discussed along with a brief literature review and some examples of case studies.

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Water Management and Sustainability in Asia
Type: Book
ISBN: 978-1-80071-114-3

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Article
Publication date: 13 February 2017

David J. Pauleen and William Y.C. Wang

This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to…

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11489

Abstract

Purpose

This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.

Design/methodology/approach

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

Findings

A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.

Research limitations/implications

This is an opinion piece, and the proposed model still needs to be empirically verified.

Practical implications

It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.

Originality/value

The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

Details

Journal of Knowledge Management, vol. 21 no. 1
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 14 February 2020

Nove E. Variant Anna and Endang Fitriyah Mannan

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…

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931

Abstract

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.

Details

Library Hi Tech News, vol. 37 no. 4
Type: Research Article
ISSN: 0741-9058

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Article
Publication date: 6 November 2017

Waqar Ahmed and Kanwal Ameen

The purpose of this paper is to define big data and draw its deep understanding. Moreover, trends of big data research in the field of library and information management…

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2285

Abstract

Purpose

The purpose of this paper is to define big data and draw its deep understanding. Moreover, trends of big data research in the field of library and information management are explored. With the purpose to explore the research trends, papers indexed in Thomson Reuters’ ISI Web of Knowledge were analyzed.

Design/methodology/approach

It is a literature-based and scientometric paper. A formal definition is constructed through a review of literature. Moreover, scientometric analysis of papers indexed in Thomson Reuters’ ISI Web of Knowledge has been done to explore the research trends associated with big data in the field of library and information science, using Vosviewer software.

Findings

The findings of the study indicate the reshaped definition of big data. The findings further indicate major research trends associated with big data. The analysis indicated “Risk”, “Industry”, “Market”, “Creditworthiness” and “Big Data Analytics”, the most repeated research trends associated with big data.

Practical implications

The paper sums up the learnings required to be a successful data-literate manager. First, the study defines big data. Second, the study describes current research trends associated with big data. Third, on the basis of the explored trends, data managers and library and information management professionals are guided about the learnings they require to be a successful data manager. Where thousands of data-literate managers are predicted to require in future, the present study is a guide to trends associated with big data.

Originality/value

It is a first study of its type which provides a reshaped definition of big data. It portrays its landscape and associated research trends in the field of information and library management (ILM).

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Article
Publication date: 23 July 2019

Emmanuel Sirimal Silva, Hossein Hassani and Dag Øivind Madsen

Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are…

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4954

Abstract

Purpose

Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of motivating and providing direction to fashion retail managers, industry experts, and academics alike, the purpose of this paper is to consider the most recent and trending applications of Big Data in fashion retailing with the aim of concisely summarising the industry’s current position and status.

Design/methodology/approach

This conceptual paper provides a brief introduction to the emerging topic of Big Data in fashion retailing by briefly synthesising findings from industry, market and academic research.

Findings

Most existing fashion brands are yet to fully engage with Big Data. The authors find that the main reasons underlying the application of Big Data analytics in fashion are trend prediction, waste reduction, consumer experience, consumer engagement and marketing, better quality control, less counterfeits and shortening of supply chains. The authors also identify key challenges which must be overcome for the most fashionable industry to be able to capitalize on Big Data to understand and predict fashion consumer behaviour.

Research limitations/implications

The brief synthesis provides a foundation for future investigations into the use of Big Data in fashion retailing.

Originality/value

This paper serves as an up-to-date introduction to how Big Data can transform fashion retailing and can act as a sound reference guide for fashion industry managers and professionals grappling with Big Data-related issues.

Details

Journal of Business Strategy, vol. 41 no. 4
Type: Research Article
ISSN: 0275-6668

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Article
Publication date: 19 October 2015

Joseph Amankwah-Amoah

Although big data have emerged at the cornerstone of business and management research, past studies have failed to offer explanations and classifications of different…

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3748

Abstract

Purpose

Although big data have emerged at the cornerstone of business and management research, past studies have failed to offer explanations and classifications of different levels of capacity and expertise possessed by different countries in utilising big data. The purpose of this paper is to examine the different capacities of governments in utilising big data.

Design/methodology/approach

The paper is based on a comprehensive synopsis of the literature on big data and the role of governments in utilising and harnessing big data.

Findings

The study provides an array of explanations to account for why some countries are adept at using big data to solve social problems, while others often faltered.

Research limitations/implications

The study offers a range of explanations and suggestions, which include skills upgrading, to help countries improve their capabilities in data collection and data analysis.

Originality/value

In this paper, data collection-data analysis matrix was developed to characterise the role of governments in data collection and analysis.

Details

Industrial Management & Data Systems, vol. 115 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 14 August 2018

Kar Hooi Tan

Although published research is limited to big data, some research focuses on the challenges that companies face in implementing big data projects. Specifically, in the…

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1569

Abstract

Purpose

Although published research is limited to big data, some research focuses on the challenges that companies face in implementing big data projects. Specifically, in the field of information systems, researchers realize that the success of Big Data projects is not only the result of data and analytics tools and processes, but also includes broader aspects. To address this issue, people have come up with a perception of big data analytics capabilities, often defined as the ability of businesses to take advantage of data management, infrastructure, and talent to turn business into competencies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings

The relationship between analytics and organizational performance has been the subject of the extant research. Prior studies have highlighted the direct influence of analytics on organizational performance. For example, big data analytics capabilities are significantly correlated with market performance and operational performance. The mechanisms through which analytics affect organizations were also examined from various perspectives.

Practical implications

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

Originality/value

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Details

Strategic Direction, vol. 34 no. 8
Type: Research Article
ISSN: 0258-0543

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Article
Publication date: 23 November 2021

Feifei Sun and Guohong Shi

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in…

Abstract

Purpose

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

Design/methodology/approach

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

Findings

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

Research limitations/implications

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

Practical implications

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

Originality/value

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 8 August 2016

H. Frank Cervone

Organizations are beginning to realize the potential benefits of big data and harnessing all of the data they are creating. However, a major impediment for many…

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3132

Abstract

Purpose

Organizations are beginning to realize the potential benefits of big data and harnessing all of the data they are creating. However, a major impediment for many organizations is understanding where to start in big data and analytics implementation. In many respects, starting a successful implementation is not much different from any other project managed within the organization. The major stumbling block is knowing what questions to ask to get things going. This paper aims to help libraries and information organizations that are considering big data and analytics implementation to begin their journey by following a checklist of eight aspects to be considered in the development of a big data and analytics strategy.

Design/methodology/approach

The eight aspects to consider in big data and analytics implementation were developed using a combination of existing project management common knowledge, consultant recommendations and real-life experiences.

Findings

Organizations considering big data and analytics implementation need to explore aspects related to the data they have, what organizational problems they are trying to solve, how data governance will work in the new environment, as well as how they will define success in terms of their implementation. These are in addition to the technical issues one would normally expect in a systems implementation.

Originality/value

While there have been many articles written about the implementation of big data and analytics in organizations, most of these focus on technical issues rather than managerial and organizational concerns. In addition, none of these other articles have been from the perspective of library and information science. In this article, the focus is specifically on how information professionals may approach this problem.

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Article
Publication date: 20 October 2021

Sumeer Gul, Shohar Bano and Taseen Shah

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself…

Abstract

Purpose

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.

Design/methodology/approach

An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.

Findings

The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.

Practical implications

The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.

Originality/value

The paper tries to highlight the current trends and facets of data mining.

Details

Digital Library Perspectives, vol. 37 no. 4
Type: Research Article
ISSN: 2059-5816

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