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Article
Publication date: 9 October 2017

Jiming Hu and Yin Zhang

The purpose of this paper is to reveal the structure and patterns of cross-national collaborations in Big Data research through application of various social network…

Abstract

Purpose

The purpose of this paper is to reveal the structure and patterns of cross-national collaborations in Big Data research through application of various social network analysis and geographical visualization methods.

Design/methodology/approach

The sample includes articles containing Big Data research, covering all years, in the Web of Science Core Collection as of December 2015. First, co-occurrence data representing collaborations among nations were extracted from author affiliations. Second, the descriptive statistics, network indicators of collaborations, and research communities were calculated. Third, topological network maps, geographical maps integrated with topological network projections, and proportional maps were produced for visualization.

Findings

The results show that the scope of international collaborations in Big Data research is broad, but the distribution among nations is unbalanced and fragmented. The USA, China, and the UK were identified as the major contributors to this research area. Five research communities are identified, led by the USA, China, Italy, South Korea, and Brazil. Collaborations within each community vary, reflecting different levels of research development. The visualizations show that nations advance in Big Data research are centralized in North America, Europe, and Asia-Pacific.

Originality/value

This study applied various informetric methods and tools to reveal the collaboration structure and patterns among nations in Big Data research. Visualized maps help shed new light on global research efforts.

Details

Journal of Documentation, vol. 73 no. 6
Type: Research Article
ISSN: 0022-0418

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Article
Publication date: 27 March 2018

Chen Chi Chang

The purpose of this paper is to explore the data connection, spatial distribution characteristics and trends in genealogical information. First, it implements a…

Abstract

Purpose

The purpose of this paper is to explore the data connection, spatial distribution characteristics and trends in genealogical information. First, it implements a spatial-temporal visualization of the Hakka genealogical information system that makes these individual family pedigree charts appear as one seamless genealogy to family and researchers seeking connections and family history all over the world. Second, this study applies migration analysis by applying big data technologies to Hakka genealogies to investigate the migration patterns of the Hakka ethnic group in Taiwan between 1954 and 2014. This innovative library service enhances the Hakka genealogical migration analysis using big data.

Design/methodology/approach

The platform is designed for the exchange of genealogical data to be used in big data analysis. This study integrates big data and geographic information systems (GIS) to map the population distribution themes. The general procedure included collecting genealogical big data, geographic encoding, gathering the map information, GIS layer integration and migration map production.

Findings

The analytical results demonstrate that big data technology is highly appropriate for family migration history analysis, given the increasing volume, velocity and variety of genealogical data. The spatial-temporal visualization of the genealogical research platform can follow family history and migration paths, and dynamically generate roadmaps to simplify the cartographic steps.

Practical implications

Technology that combines big data and GIS is suitable for performing migration analysis based on genealogy. A web-based application for spatial-temporal genealogical information also demonstrates the contribution of innovative library services.

Social implications

Big data play a dominant role in library services, and in turn, provide an active library service. These findings indicate that big data technology can provide a suitable tool for improving library services.

Originality/value

Online genealogy and family trees are linked with large-volume, growing data sets that are complex and have multiple, autonomous sources. The migration analysis using big data has the potential to help genealogy researchers to construct minority ethnic history.

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Article
Publication date: 7 August 2017

Zhichao Fang, Xinhui Guo, Yang Yang, Zhongkai Yang, Qingchun Li, Zhigang Hu and Xianwen Wang

This study aims to analyse the geographical distribution of global research activities and to investigate the knowledge diffusion embodied in scientific papers.

Abstract

Purpose

This study aims to analyse the geographical distribution of global research activities and to investigate the knowledge diffusion embodied in scientific papers.

Design/methodology/approach

The geographical summary of Frontiers articles displays the number of visits and categorizes where the visitors hail from. This study uses the records of 23,798 articles published in 16 Frontiers journals from 2007 to 2015 to analyse the geographical distribution of article visits at both country and city levels. The process of knowledge diffusion is investigated on the basis of the different visiting patterns of new and old papers.

Findings

Most article visits are concentrated around major metropolitan areas and some high-tech clusters. The top “visiting countries” include both developed countries and developing countries, and the USA and China are two major players. Publishing cities dominate article visits for new papers; as time passes, there is diffusion from the publishing cities to a broader area.

Research limitations/implications

The data on visiting for open access articles may be generated from various repositories besides the publishers’ websites; these data are ignored, as they are not significant enough to have much influence. There is also a lack of a basic theory in the data processing of outliers in the data set. In addition, only static results are given in this paper, as the data were collected on one day, for one time. A longer time period is necessary to track the dynamic diffusion process of the observations.

Practical implications

Introduction of usage data will propose a novel way to analyse research activities and track knowledge diffusion.

Social implications

The visiting data of articles offer a new way to investigate research activities at the city level in a detailed and timely manner, for the geographical distribution of research activities and the research resource allocation of a specific country to be explored.

Originality/value

This study measured the research activities of scientific papers by examining the usage data. Compared with previous studies that focused on the geographical distribution of scientific activities using publication data, citation data and even altmetrics data, usage data are at the forefront of this research. Therefore, usage data offer a fresh perspective on methodology, providing more detailed and real-time information.

Details

The Electronic Library, vol. 35 no. 4
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 29 January 2020

Chao Fu, Qing Lv and Reza G. Badrnejad

Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and…

Abstract

Purpose

Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works.

Design/methodology/approach

The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms.

Findings

The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing.

Research limitations/implications

This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English.

Practical implications

The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future.

Originality/value

Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.

Details

Kybernetes, vol. 49 no. 12
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 5 June 2020

Hanyoung Go, Myunghwa Kang and Yunwoo Nam

This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and…

Abstract

Purpose

This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites.

Design/methodology/approach

The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world.

Findings

This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search.

Originality/value

This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

摘要

研究目的本论文旨在探索生态旅游业在电子世界中是如何随着时间而显示出来的,文章样本为带有地理标记的图片和互联网搜索数据。本文使用生态旅游图片和谷歌趋势搜索数据来评估游客对生态旅游的感知,通过对关键要素的分类,审视生态旅游和可持续发展的关系,以及衡量生态旅游基地的受欢迎程度等方法。

研究设计/方法/途径

本论文作者从Flickr.com上搜集地理标记图片以及从谷歌趋势上下载谷歌搜索数据。样本分析通过内容、趋势、空间上的综合分析,来开发生态旅游类别和游客对生态旅游的感知。首先,我们研究了社交媒体上的生态旅游地理标记图片以理解游客对生态旅游的感知情况,以此搭建了关键生态旅游要素的类别体系,和衡量生态旅游基地的受欢迎程度。第二,我们通过使用互联网搜索数据,检测了生态旅游如何与可持续发展相连接,以及研究了搜索数据中的趋势。第三,我们使用了GIS软件来操作空间分析,对图片的空间-时间改变和游客对世界的观点做了可视化处理。

研究结果

本论文确立了三项生态旅游感知的基本主题以及13项生态旅游要素类别。生态旅游互联网随着时间演化,根据谷歌趋势上的定义,被大致地展现出来。本论文研究结果表示生态旅游地理位置和游客足迹与生态旅游谷歌搜索的热门区域不全是完全吻合的。

研究原创性/价值

本论文使用地理标记图片和互联网搜索数据将生态旅游发展趋势近十年的变化描画出来。全球生态旅游趋势的评估对全球可持续旅游发展和实际游客感知方面做出重要见解启示。生态旅游趋势的分析作为一种战略方法,对UN可持续发展目标的时间起到评估作用。本论文针对游客的真实感知和意见,游客如何选择和感知自然景观,这对于很多群体,比如旅游行业和政府,都有着重要意义。

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Book part
Publication date: 6 December 2018

Janet Mifsud and Cristina Gavrilovici

Big Data analysis is one of the key challenges to the provision of health care to emerge in the last few years. This challenge has been spearheaded by the huge interest in…

Abstract

Big Data analysis is one of the key challenges to the provision of health care to emerge in the last few years. This challenge has been spearheaded by the huge interest in the “4Ps” of health care (predictive, preventive, personalized, and participatory). Big Data offers striking development opportunities in health care and life sciences. Healthcare research is already using Big Data to analyze the spatial distribution of diseases such as diabetes mellitus at detailed geographic levels. Big Data is also being used to assess location-specific risk factors based on data of health insurance claims. Other studies in systems medicine utilize bioinformatics approaches to human biology which necessitate Big Data statistical analysis and medical informatics tools. Big Data is also being used to develop electronic algorithms to forecast clinical events in real time, with the intent to improve patient outcomes and thus reduce costs.

Yet, this Big Data era also poses critically difficult ethical challenges, since it is breaking down the traditional divisions between what belongs to public and private domains in health care and health research. Big Data in health care raises complex ethical concerns due to use of huge datasets obtained from different sources for varying reasons. The clinical translation of this Big Data is thus resulting in key ethical and epistemological challenges for those who use these data to generate new knowledge and the clinicians who eventually apply it to improve patient care.

Underlying this challenge is the fact that patient consent often cannot be collected for the use of individuals’ personal data which then forms part of this Big Data. There is also the added dichotomy of healthcare providers which use such Big Data in attempts to reduce healthcare costs, and the negative impact this may have on the individual with respect to privacy issues and potential discrimination.

Big Data thus challenges societal norms of privacy and consent. Many questions are being raised on how these huge masses of data can be managed into valuable information and meaningful knowledge, while still maintaining ethical norms. Maintaining ethical integrity may lack behind in such a fast-changing sphere of knowledge. There is also an urgent need for international cooperation and standards when considering the ethical implications of the use of Big Data-intensive information.

This chapter will consider some of the main ethical aspects of this fast-developing field in the provision of health care, health research, and public health. It will use examples to concretize the discussion, such as the ethical aspects of the applications of Big Data obtained from clinical trials, and the use of Big Data obtained from the increasing popularity of health mobile apps and social media sites.

Details

Ethics and Integrity in Health and Life Sciences Research
Type: Book
ISBN: 978-1-78743-572-8

Keywords

Content available

Abstract

Details

Management Decision, vol. 57 no. 8
Type: Research Article
ISSN: 0025-1747

Content available

Abstract

Details

Library Hi Tech, vol. 36 no. 3
Type: Research Article
ISSN: 0737-8831

Content available
Article
Publication date: 16 September 2020

Nilanjan Raghunath and Tony Tan

Socioeconomic status (SES) has been known to be associated with many aspects of social life such as health. We argue that social stratification remains relevant in…

Abstract

Purpose

Socioeconomic status (SES) has been known to be associated with many aspects of social life such as health. We argue that social stratification remains relevant in understanding differentials in health during a pandemic, as countries globally have encouraged or enforced social distancing and remote work measures.

Design/methodology/approach

By examining data sources and news reports on the COVID-19 pandemic, we aim to highlight the relationship between SES and morbidity, through the ability to adopt social distancing measures and work remotely. Utilizing publicly available data from the Maryland Transport Institute and the US Census, we performed linear regressions on median income, social distancing index and percentage of individuals working from home.

Findings

Individuals with higher SES are more likely to have jobs that provide opportunities for remote work to be performed, which allows for social distancing. Comparatively, individuals with lower SES are more likely to be involved in jobs that cannot be performed remotely. The linear regression models suggest a positive moderate and significant correlation between median income and social distancing index (R2 = 0.4981, p-value < 0.001), and a positive weak and significant correlation between median income and remote work (R2 = 0.2460, p-value < 0.001).

Research limitations/implications

Governments need to account for SES in policymaking to reduce inequalities in health.

Originality/value

The paper aims to improve the understanding of social stratification and morbidity through examining data on the COVID-19 pandemic.

Details

International Journal of Sociology and Social Policy, vol. 40 no. 9/10
Type: Research Article
ISSN: 0144-333X

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Article
Publication date: 20 May 2020

Luana Serino, Armando Papa, Francesco Campanella and Leonardo Di Gioia

We explore the impact of a specific knowledge management framework on the quality of innovation, the geographic distribution of R&D and the cross-regional integration. We…

Abstract

Purpose

We explore the impact of a specific knowledge management framework on the quality of innovation, the geographic distribution of R&D and the cross-regional integration. We use directly observed indicators of cross-regional knowledge application within the firm as well as examine collaborative mechanisms that firms may use to promote such knowledge translation.

Design/methodology/approach

Our analysis is based upon successful patents in biotechnology sector applied for during 2011–2014. The empirical assessment follows a mixed method approach. The sample used for testing the empirical hypotheses is composed of 130,720 patents from 860 large US firms. The sample of patents was obtained from USPTO and NBER dataset.

Findings

The idea of this paper was to introduce a model specifically developed for the process of knowledge translation. This research contributes to the literature related to the emergent and new issue namely collaborative knowledge translation (CKT), especially emphasizing the key role of the knowledge translation practices and tools for the internationalization of R&D teams and supporting the quality of innovations in different ways.

Originality/value

This research is conceptually based on the broader concepts of spiral of knowledge of Nonaka and Takeuchi. In spite of the increasing research in innovation, few studies have been done about the diverse contexts' role in the knowledge flows supporting the innovation development. In the attempt to cover this gap, the objective of this research is to answer the following main research question: How to support and manage the process of knowledge translation in innovation processes occurring in collaborative teams?

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