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Journal ArticleDOI

Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study

01 Apr 2017-Scientometrics (Springer Netherlands)-Vol. 111, Iss: 1, pp 463-492
TL;DR: A bibliometric literature review from the Scopus Database over the period of 2006–2015 focusing on the application of big data/IoT on the context of CE indicates China and USA are the most interested countries in the area and reveal a context with significant opportunities for research.
Abstract: Circular economy (CE) is a term that exists since the 1970s and has acquired greater importance in the past few years, partly due to the scarcity of natural resources available in the environment and changes in consumer behavior. Cutting-edge technologies such as big data and internet of things (IoT) have the potential to leverage the adoption of CE concepts by organizations and society, becoming more present in our daily lives. Therefore, it is fundamentally important for researchers interested in this subject to understand the status quo of studies being undertaken worldwide and to have the overall picture of it. We conducted a bibliometric literature review from the Scopus Database over the period of 2006---2015 focusing on the application of big data/IoT on the context of CE. This produced the combination of 30,557 CE documents with 32,550 unique big data/IoT studies resulting in 70 matching publications that went through content and social network analysis with the use of `R' statistical tool. We then compared it to some current industry initiatives. Bibliometrics findings indicate China and USA are the most interested countries in the area and reveal a context with significant opportunities for research. In addition, large producers of greenhouse gas emissions, such as Brazil and Russia, still lack studies in the area. Also, a disconnection between important industry initiatives and scientific research seems to exist. The results can be useful for institutions and researchers worldwide to understand potential research gaps and to focus future investments/studies in the field.
Citations
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01 Jan 1995
TL;DR: In this paper, the authors propose a method to improve the quality of the data collected by the data collection system. But it is difficult to implement and time consuming and computationally expensive.
Abstract: 本文对国际科学计量学杂志《Scientometrics》1979-1991年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析,揭示出科学计量学研究的重点、活动的中心及发展趋势,说明了学科带头人在发展科学计量学这门新兴学科中的作用。

1,636 citations

Journal ArticleDOI
TL;DR: A systematic literature review exploring the state-of-the-art of academic research on circular economy (CE) is presented in this paper, where the authors examine the CE body of literature with a systematic approach, to provide an exhaustive analysis of the phenomenon with rigorous and reproducible research criteria.

698 citations

Journal ArticleDOI
TL;DR: In this article, a comprehensive study on various factors, that affects the sustainable supply chain were analyzed and the results recorded, based on the review, a framework for assessing the readiness of supply chain organization from various perspectives has been proposed to meet the requirements of the fourth Industrial Revolution.

526 citations

References
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Journal Article
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

272,030 citations

Book
13 Aug 2009
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Abstract: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, its easy to: produce handsome, publication-quality plots, with automatic legends created from the plot specification superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots approach your graph from a visual perspective, thinking about how each component of the data is represented on the final plot. This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems,and youll find it easy to get graphics out of your head and on to the screen or page.

29,504 citations

01 Jan 2006
TL;DR: Platform-independent and open source igraph aims to satisfy all the requirements of a graph package while possibly remaining easy to use in interactive mode as well.
Abstract: There is no other package around that satisfies all the following requirements: •Ability to handle large graphs efficiently •Embeddable into higher level environments (like R [6] or Python [7]) •Ability to be used for quick prototyping of new algorithms (impossible with “click & play” interfaces) •Platform-independent and open source igraph aims to satisfy all these requirements while possibly remaining easy to use in interactive mode as well.

8,850 citations


"Scientific literature analysis on b..." refers methods in this paper

  • ...Results were obtained with the use of R statistical software (R Core Team 2016), and R additional packages (Feinerer et al. 2008; Bouchet-Valat 2014; Wickham 2009; Fellows 2014; Hornik et al. 2009; Csardi and Nepusz 2006)....

    [...]

Journal ArticleDOI

5,590 citations


"Scientific literature analysis on b..." refers methods in this paper

  • ...Results were obtained with the use of R statistical software (R Core Team 2016), and R additional packages (Feinerer et al. 2008; Bouchet-Valat 2014; Wickham 2009; Fellows 2014; Hornik et al. 2009; Csardi and Nepusz 2006)....

    [...]

Journal ArticleDOI
TL;DR: The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday.
Abstract: It has been next to impossible in the past months not to come across the term ‘‘Internet of Things’’ (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the Internet of Things. Consortia have been formed to define frameworks and standards for the IoT. Companies have started to introduce numerous IoTbased products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for $3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth $7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses. The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology (MIT) on networked radio-frequency identification (RFID) infrastructures (Atzori et al. 2010; Mattern and Floerkemeier 2010). Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) for instance now defines the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies’’ (ITU 2012). At the same time, a multitude of alternative definitions has been proposed. Some of these definitions exhibit an emphasis on the things which become connected in the IoT. Other definitions focus on Internet-related aspects of the IoT, such as Internet protocols and network technology. And a third type centers on semantic challenges in the IoT relating to, e.g., the storage, search and organization of large volumes of information (Atzori et al. 2010). The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday. The most prominent areas of application include, e.g., the smart industry, where the development of intelligent production systems and connected production sites is often discussed under the heading of Industry 4.0. In the smart home or building area, intelligent thermostats and security systems are receiving a lot of attention, while smart energy applications focus on smart electricity, gas and water meters. Smart transport solutions include, e.g., vehicle fleet tracking and mobile ticketing, while in the smart health area, topics such as patients’ surveillance and chronic disease management are being addressed. And in the context of Accepted after one revision by Prof. Dr. Sinz.

3,499 citations