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Big Data for Sustainable Anticipatory Computing
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Big Data for Sustainable Anticipatory Computing

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 60351

Special Issue Editors

Dr. Jason C. Hung

E-Mail Website
Guest Editor
Department of Information Technology, Overseas Chinese University, No:100, Chiao Kwang Rd., Taichung 407, Taiwan
Interests: multimedia systems; intelligent computing; e-learning; social computing; location-based service; anticipatory computing
Special Issues, Collections and Topics in MDPI journals
Dr. Neil Yuwen Yen

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
Interests: human-centric computing; big data; anticipatory computing
Special Issues, Collections and Topics in MDPI journals
Prof. Francisco Isidro Massetto

E-Mail Website
Guest Editor
Federal University of ABC, Brazil
Interests: distributed systems; high-performance computing; distributed computing; grid computing and hybrid systems
Dr. Jia-Wei Chang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City 404348, Taiwan
Interests: natural language processing; data mining; artificial intelligence; e-learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Anticipatory computing is an emerging paradigm. This computing paradigm attempts to revolutionize how the services are provided as well as to enable better decision-making based on the contexts obtained from the scenario. Anticipatory Computing responds to your query and you receive a list of results, which could add up to thousands of pages that you may have to browse and cull through to find the information that you think you seek. Anticipatory computing flips this paradigm. Rather than search and weed through results, the “right” information finds you by anticipating what you will need and when you will need it. While there are manifold potential applications for anticipatory computing (for instance, the MindMeld iPad app, powered by an anticipatory computing search engine that combines mobile, voice recognition, and big data), anticipatory computing, in this context, can create a “learning layer” that can help organizations learn more quickly and work smarter.

This Special Issue aims to provide an overview of the state-of-the-art of issues and solution guidelines for the SAC (Sustainable Anticipatory Computing). In addition, it will complete the panorama of the current research effort, which is widely inherent to topics of high interest for sustainable anticipatory computing.

Prof. Jason C. Hung
Prof. Neil Yen
Prof. Francisco Isidro Massetto
Prof. Jia-Wei Chang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data for sustainable anticipatory computing
  • Sustainable IoT for anticipatory computing
  • Recommendation system for sustainable anticipatory computing
  • Financial Technology (FinTech) for sustainable business
  • Anticipatory computing for sustainable institutional research
  • New-trend technology for sustainable anticipatory computing
  • Human-centered anticipatory computing for sustainable environment
  • Blockchain Technology for sustainable application
  • Artificial intelligence for sustainable application

Published Papers (16 papers)

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Research

11 pages, 20109 KiB  
Article
The Discussion of Potential Care Needs for Physically and Mentally Disabled Citizens in Taipei City by Using Spatial Analysis
by Jui-Hung Kao, Wei-Chen Wu, Cheng-Hu Chow and Horng-Twu Liaw
Sustainability 2021, 13(5), 2665; https://0-doi-org.brum.beds.ac.uk/10.3390/su13052665 - 02 Mar 2021
Cited by 1 | Viewed by 1391
Abstract
What this research may achieve points towards the need to progressively improve the reasonableness in establishing Social Welfare Agencies (SWAs). The service capacity of SWAs is far below the population of the level III extremely disabled. This is a serious problem. This evaluation [...] Read more.
What this research may achieve points towards the need to progressively improve the reasonableness in establishing Social Welfare Agencies (SWAs). The service capacity of SWAs is far below the population of the level III extremely disabled. This is a serious problem. This evaluation can assist social welfare and public health departments to determine what locations to approve for establishing SWAs in the short term and plan for new SWAs more precisely, as well as rein in budgetary priorities. As an illustration, in considering the distance between SWAs and the extremely disabled, the service quality of SWAs and fairness in the planning have to be taken into account. Introducing a Service Quantity Needed-Index for SWAs (SNIS) into the current measure of approving and planning new SWAs shall assist the departments in distributing social welfare resources to areas most in need of help. In addition, using the modified data to recalculate SNIS can examine needs regularly. Employing basic statistical areas for short-term applications in Taipei City SWA projects, considering the distance between SWAs and the extremely disabled, the agencies’ service quality and fairness in the planning of SWAs need to receive more attention. Previous research mostly employed straight-line distances rather than road distances. To a certain extent, this overlooked the actual capacity of roads as well as led to some degree of discrepancies in evaluations. This essay focuses on calculating SNIS, mainly towards guiding the establishment of facilities and concretely proposing how to optimize their locations. Future research can add in needs at that time in accordance with current evaluation results to propose plans to optimize the locations, or maybe integrate weights of disability to adjust multiple requirements of SWAs. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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17 pages, 4695 KiB  
Article
Implement an International Interoperable PHR by FHIR—A Taiwan Innovative Application
by Yen-Liang Lee, Hsiu-An Lee, Chien-Yeh Hsu, Hsin-Hua Kung and Hung-Wen Chiu
Sustainability 2021, 13(1), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/su13010198 - 28 Dec 2020
Cited by 7 | Viewed by 4878
Abstract
Personal health records (PHRs) have lots of benefits for things such as health surveillance, epidemiological surveillance, self-control, links to various services, public health and health management, and international surveillance. The implementation of an international standard for interoperability is essential to accessing personal health [...] Read more.
Personal health records (PHRs) have lots of benefits for things such as health surveillance, epidemiological surveillance, self-control, links to various services, public health and health management, and international surveillance. The implementation of an international standard for interoperability is essential to accessing personal health records. In Taiwan, the nationwide exchange platform for electronic medical records (EMRs) has been in use for many years. The Health Level Seven International (HL7) Clinical Document Architecture (CDA) was used as the standard of the EMRs. However, the complication of implementing CDA became a barrier for many hospitals to realize the standard EMRs. In this study, we implemented a Fast Healthcare Interoperability Resources (FHIR)-based PHR transformation process including a user interface module to review the contents of PHRs. We used “My Health Bank, MHB”, a PHR data book developed and issued to all people by the Taiwan National Health Insurance, as the PHRs contents in this study. Network Time Protocol (NTP)/Simple Network Time Protocol (SNTP) was used in the security and user authentication mechanism when processing and applying personal health information. Transport Layer Security (TLS) 1.2 (such as HyperText Transfer Protocol Secure (HTTPS) was used for protection in data communication. User authentication is important in the platform. OAuth (OAuth 2.0) was used as a user authentication mechanism to confirm legitimate user access to ensure data security. The contents of MHB were analyzed and mapped to the FHIR, and then converted to FHIR format according to the mapping logic template. The function of format conversion was carried out by using ASP.NET. XPath and JSPath technologies filtered out specific information tags. The converted data structure was verified through an HL7 Application Programming Interface (HAPI) server, and a new JSON file was finally created. This platform can not only capture any PHR based on the FHIR format but also publish FHIR-based MHB records to any other platform to bridge the interoperability gap between different PHR systems. Therefore, our implementation/application with the automatic transformation from MHB to FHIR format provides an innovative method for people to access their own PHRs (MHB). No one has published a similar application like us using a nationwide PHR standard, MHB, in Taiwan. The application we developed will be very useful for a single person to use or for other system developers to implement their own standard PHR software. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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17 pages, 2362 KiB  
Article
Implementation for Comparison Analysis System of Used Transaction Using Big Data
by Byungjoon Park, Hasung Kim and Byeongtae Ahn
Sustainability 2020, 12(19), 8029; https://doi.org/10.3390/su12198029 - 29 Sep 2020
Cited by 1 | Viewed by 2809
Abstract
With the recent increase in used trading sites that support used trading, users want to find various information in real time, and the development of the Internet consists of direct and indirect connections between businesses and consumers. This change created a new type [...] Read more.
With the recent increase in used trading sites that support used trading, users want to find various information in real time, and the development of the Internet consists of direct and indirect connections between businesses and consumers. This change created a new type of C2C (Commerce to Commerce) transaction. However, each used trading site has its own characteristics, making it difficult to standardize one. Therefore, in this paper, we construed a system that provides the user’s used transaction data in real time and provides the desired information quickly. In this paper, we developed the crawler system needed to develop an integrated transaction system for second-hand goods through Internet e-commerce transactions, defined morphological analyzers, and described the service that users can employ in the web environment by using the system developed in the paper. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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25 pages, 3716 KiB  
Article
Development of a New Methodology to Identity Promising Technology Areas Using M&A Information
by Jinho Choi and Yong Sik Chang
Sustainability 2020, 12(14), 5606; https://0-doi-org.brum.beds.ac.uk/10.3390/su12145606 - 12 Jul 2020
Cited by 1 | Viewed by 2140
Abstract
In this paper, we suggest a new methodology to identify promising technology areas by analyzing merger and acquisition (M&A) information. First, we present decision models for estimating the velocity and acceleration of M&A transactions to identify promising areas based on M&A information. Second, [...] Read more.
In this paper, we suggest a new methodology to identify promising technology areas by analyzing merger and acquisition (M&A) information. First, we present decision models for estimating the velocity and acceleration of M&A transactions to identify promising areas based on M&A information. Second, we identify the promising technology areas with longitudinal analyses of M&As over the entire period. Third, cross-sectional analysis is proposed to determine which technology areas are more promising through a relative comparison among technology areas within the IT sector for a specific period. The main significance of our research is that it is a prior data-based analytic method based on M&A transaction information to identify the growth of industry and technology. We hope this study will provide insights for R&D (Research&Development) policymakers and investment firms as a new approach that complements previous methods in exploring promising industry or technology areas. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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17 pages, 4086 KiB  
Article
ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation
by Yi-Zeng Hsieh, Shih-Syun Lin, Yu-Cin Luo, Yu-Lin Jeng, Shih-Wei Tan, Chao-Rong Chen and Pei-Ying Chiang
Sustainability 2020, 12(14), 5605; https://0-doi-org.brum.beds.ac.uk/10.3390/su12145605 - 12 Jul 2020
Cited by 21 | Viewed by 3890
Abstract
Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning [...] Read more.
Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning information; in turn, the enhanced education contributes to the rapid development of science and technology. Education is sustainable life learning, as well as the most important promoter of science and technology worldwide. In recent years, a large number of anticipatory computing applications based on AI have promoted the training professional AI talent. As a result, this study aims to design a set of interactive robot-assisted teaching for classroom setting to help students overcoming academic difficulties. Teachers, students, and robots in the classroom can interact with each other through the ARCS motivation model in programming. The proposed method can help students to develop the motivation, relevance, and confidence in learning, thus enhancing their learning effectiveness. The robot, like a teaching assistant, can help students solving problems in the classroom by answering questions and evaluating students’ answers in natural and responsive interactions. The natural interactive responses of the robot are achieved through the use of a database of emotional big data (Google facial expression comparison dataset). The robot is loaded with an emotion recognition system to assess the moods of the students through their expressions and sounds, and then offer corresponding emotional responses. The robot is able to communicate naturally with the students, thereby attracting their attention, triggering their learning motivation, and improving their learning effectiveness. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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14 pages, 1555 KiB  
Article
Computing the Assembly Guidance for Maximizing Product Quality in the Virtual Assembly
by Chen-Kun Tsung, Tseng-Fung Ho, Hsuan-Yu Huang, Shu-Hui Yang, Po-Nien Tsou, Ming-Cheng Tsai and Yi-Ping Huang
Sustainability 2020, 12(11), 4690; https://0-doi-org.brum.beds.ac.uk/10.3390/su12114690 - 08 Jun 2020
Cited by 2 | Viewed by 2113
Abstract
Assembly is the final process of manufacturing, and a good assembly plan reduces the effect of the tolerance generated in the early stages by the tolerance elimination. In the current assembly lines, the assemblers pick up the workpieces and install them together by [...] Read more.
Assembly is the final process of manufacturing, and a good assembly plan reduces the effect of the tolerance generated in the early stages by the tolerance elimination. In the current assembly lines, the assemblers pick up the workpieces and install them together by the assembly instructions. When the workpieces are oversize or undersize, the product can not be installed correctly. Therefore, the assembler considers the secondary processing to fix the tolerance and then installs them together again. The product could be installed, but the product quality may be reduced by the secondary process. So, we formulate the assembly process as a combinatorial optimization problem, named by the dimensional chain assembly (DCA) problem. Given some workpieces with the corresponding actual size, computing the assembly guidance is the goal of the DCA problem, and the product quality is applied to represent the solution quality. The assemblers follow the assembly guidance to install the products. We firstly prove that the DCA problem is NP-complete and collect the requirements of solving the DCA problem from the implementation perspective: the sustainability, the minimization of computation time, and the guarantee of product quality. We consider solution refinement and the solution property inheritance of the single-solution evolution approach to discover and refine the quality of the assembly guidance. Based on the above strategies, we propose the assembly guidance optimizer (AGO) based on the simulated annealing algorithm to compute the assembly guidance. From the simulation results, the AGO reaches all requirements of the DCA problem. The variance of the computation time and the solution quality is related to the problem scale linearly, so the computation time and the solution quality can be estimated by the problem scale. Moreover, increasing the search breadth is unnecessary for improving the solution quality. In summary, the proposed AGO satisfies with the necessaries of the sustainability, the minimization of computation time, and the guarantee of product quality for the requirements of the DCA, and it can be considered in the real-world applications. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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17 pages, 1562 KiB  
Article
Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology
by Changgyun Kim and Sekyoung Youm
Sustainability 2020, 12(9), 3702; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093702 - 03 May 2020
Cited by 1 | Viewed by 2905
Abstract
Measuring exact obesity rates is challenging because the existing measures, such as body mass index (BMI) and waist-to-height ratio (WHtR), do not account for various body metrics and types. Therefore, these measures are insufficient for use as health indices. This study presents a [...] Read more.
Measuring exact obesity rates is challenging because the existing measures, such as body mass index (BMI) and waist-to-height ratio (WHtR), do not account for various body metrics and types. Therefore, these measures are insufficient for use as health indices. This study presents a model that accurately classifies abdominal obesity, or muscular obesity, which cannot be diagnosed with BMI. Using the model, a web-based calculator was created, which provides information on obesity by predicting healthy ranges, and obesity, underweight, and overweight values. For this study, musculoskeletal mass and body composition mass data were obtained from Size Korea. The groups were divided into four groups, and six body circumference values were used to classify the obesity levels. Of the four learning models, the random forest model was used and had the highest accuracy (99%). This enabled us to build a web-based tool that can be accessed from anywhere and can measure obesity information in real-time. Therefore, users can quickly receive and update their own obesity information without using existing high-cost equipment (e.g., an Inbody machine or a body-composition analyzer), thereby making self-diagnosis convenient. With this model, it was easy to recognize and manage health conditions by quickly receiving and updating information on obesity without using traditional, expensive equipment, and by providing accurate information on obesity, according to body types, rather than information such as BMI, which are identified based on specific body characteristics. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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17 pages, 1231 KiB  
Article
Intention to Use Sustainable Green Logistics Platforms
by Su-Young Kwak, Woo-Sung Cho, Gil-Am Seok and Seung-Gyun Yoo
Sustainability 2020, 12(8), 3502; https://0-doi-org.brum.beds.ac.uk/10.3390/su12083502 - 24 Apr 2020
Cited by 15 | Viewed by 5515
Abstract
Recently, logistics platforms that facilitate interaction and the exchange and transaction of information have quickly emerged in the Korean domestic market. In order to further advance the development of logistics platforms into green logistics platforms in which participation in the early stages is [...] Read more.
Recently, logistics platforms that facilitate interaction and the exchange and transaction of information have quickly emerged in the Korean domestic market. In order to further advance the development of logistics platforms into green logistics platforms in which participation in the early stages is not active, appropriate checks and balances are needed so that service providers, users, and platform operators can grow together in green logistics platforms. The purpose of this study is to empirically verify the factors affecting participants’ intentions to use green logistics platforms. Out of the 230 questionnaires distributed from 25 June to 11 July 2019, 14 were excluded from analyses due to unsatisfactory responses, while 216 responses were used for statistical processing. The structural equation model (SEM) was used to test hypotheses in this research. The results showed that the network effect and security factors influenced perceived usefulness, and trust did not affect perceived usefulness. Perceived usefulness also significantly influenced the intention to use green logistics platforms. The results of this study present strategies and directions for the future development of green logistics platforms. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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13 pages, 5553 KiB  
Article
Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT
by Seok-Woo Jang and Byeongtae Ahn
Sustainability 2020, 12(7), 3037; https://0-doi-org.brum.beds.ac.uk/10.3390/su12073037 - 10 Apr 2020
Cited by 26 | Viewed by 7940
Abstract
In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and [...] Read more.
In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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15 pages, 13973 KiB  
Article
Implementation of Automated Baby Monitoring: CCBeBe
by Soohyun Choi, Songho Yun and Byeongtae Ahn
Sustainability 2020, 12(6), 2513; https://0-doi-org.brum.beds.ac.uk/10.3390/su12062513 - 23 Mar 2020
Cited by 6 | Viewed by 4926
Abstract
An automated baby monitoring service CCBeBe (CCtv Bebe) monitors infants’ lying posture and crying based on AI and provides parents-to-baby video streaming and voice transmission. Besides, parents can get a three-minute daily video diary made by detecting the baby’s emotion such as happiness. [...] Read more.
An automated baby monitoring service CCBeBe (CCtv Bebe) monitors infants’ lying posture and crying based on AI and provides parents-to-baby video streaming and voice transmission. Besides, parents can get a three-minute daily video diary made by detecting the baby’s emotion such as happiness. These main features are based on OpenPose, EfficientNet, WebRTC, and Facial-Expression-Recognition.Pytorch. The service is integrated into an Android application and works on two paired smartphones, with lowered hardware dependence. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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20 pages, 3465 KiB  
Article
Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing
by Seok-Woo Jang and Sang-Hong Lee
Sustainability 2020, 12(6), 2373; https://0-doi-org.brum.beds.ac.uk/10.3390/su12062373 - 18 Mar 2020
Cited by 1 | Viewed by 1866
Abstract
High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges [...] Read more.
High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects’ regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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13 pages, 408 KiB  
Article
The Effects of Knowledge Assets and Path Dependence in Innovations on Firm Value in the Korean Semiconductor Industry
by Yoonkyo Cho
Sustainability 2020, 12(6), 2319; https://0-doi-org.brum.beds.ac.uk/10.3390/su12062319 - 16 Mar 2020
Cited by 8 | Viewed by 2973
Abstract
This study investigated whether firms’ knowledge assets and path dependence in their innovations affect firm value. For the analysis, I used 37 firms in the semiconductor industry in Korea. These firms were listed on the Korea Stock Exchange and the Korea Securities Dealers [...] Read more.
This study investigated whether firms’ knowledge assets and path dependence in their innovations affect firm value. For the analysis, I used 37 firms in the semiconductor industry in Korea. These firms were listed on the Korea Stock Exchange and the Korea Securities Dealers Association Automated Quotation as of 2010 and through 2015. The dependent variable was measured by return on assets as firm value, and the ordinary least squares estimation was used. The results showed that a firm’s knowledge assets have a positive effect on firm value. In addition, when a firm creates new knowledge, if the firm follows path dependence by using its own knowledge, it has a positive effect on firm value. By contrast, when a firm conducts innovations using knowledge created by other firms, it has no effect on the value of the firm. Additionally, I found that technological innovation based on knowledge assets and path dependence has a positive effect on firm value in the short term but has no effect in the medium term. Thus, firms need to continue their innovation to maintain their competitive advantage and to use their existing knowledge in innovation in order to have high performance. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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20 pages, 6593 KiB  
Article
The History and Evolution: A Big Data Analysis of the National Innovation Systems in South Korea
by Eun Sun Kim, Kuk Jin Bae and Jeongeun Byun
Sustainability 2020, 12(3), 1266; https://0-doi-org.brum.beds.ac.uk/10.3390/su12031266 - 10 Feb 2020
Cited by 6 | Viewed by 5554
Abstract
This study is a starting point to analyze South Korean national innovation systems (KNIS) using big data and provide insights for policy makers regarding how they implement the dynamic process of innovation systems. It examines KNIS that has developed over the past 14 [...] Read more.
This study is a starting point to analyze South Korean national innovation systems (KNIS) using big data and provide insights for policy makers regarding how they implement the dynamic process of innovation systems. It examines KNIS that has developed over the past 14 years from 2003 to 2016 during the governments of Roh Moo-hyun, Lee Myung-bak, and Park Geun-hye. The aim of this study is to evaluate the KNIS in three ways. The first way is to analyze the NIS of the three governments based on data of 470,000 national research and development (R&D) projects, following which the second way is to compare innovative outcomes of the three governments. The last way is to figure out the characteristics of the KNIS in innovative performance. Our analysis reveals that the KNIS was developed and evolved from 2003 to 2008, maintained until 2012, and gradually declined, even though national R&D investment increased for 14 years. Empirical evidence highlights that policies implemented for more than a decade do not effectively link to economic outcomes, resulting in an imbalance between innovation input and innovation output. This study further argues that the use of NIS concept in South Korea seems to be skewed towards measuring national performance from a narrower perspective. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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14 pages, 1937 KiB  
Article
Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs
by Eun Sun Kim, Yunjeong Choi and Jeongeun Byun
Sustainability 2020, 12(1), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/su12010202 - 25 Dec 2019
Cited by 15 | Viewed by 5285
Abstract
To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small [...] Read more.
To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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19 pages, 1469 KiB  
Article
Is M&A Information Useful for Exploring Promising Industries and Technologies?
by Jinho Choi, Sunghun Chung and Yong Sik Chang
Sustainability 2020, 12(1), 139; https://0-doi-org.brum.beds.ac.uk/10.3390/su12010139 - 23 Dec 2019
Cited by 3 | Viewed by 2755
Abstract
Companies today that seek to diversify their business are looking for opportunities in new markets by considering their core competencies. However, companies are struggling to diversify and grow their current businesses due to a lack of information concerning diversification and a low level [...] Read more.
Companies today that seek to diversify their business are looking for opportunities in new markets by considering their core competencies. However, companies are struggling to diversify and grow their current businesses due to a lack of information concerning diversification and a low level of capability for future commercialization. In this study, we suggest a new methodology that identifies promising industry and technology areas by examining mergers and acquisitions (M&As) transaction data. Specifically, by analyzing the extent to which firms have engaged in M&A activities, the prediction of promising industries is derived from the relationships among specific industries, as well as the M&A transactions among technology areas within a focal industry. We first theoretically test whether all M&A transactions are related to promising areas. Second, we analyze the trends of global M&As by a time-series analysis of M&A transactions by sectors over the last 15 years. Lastly, we conduct an association analysis to identify the degree of M&A connections between industry and technology areas, respectively. We hope that our results provide insights for R&D policymakers and investors who need to decide on promising industries to cultivate or invest in, and researchers who want to identify overall M&A trends and promising industries and technology areas. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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17 pages, 3611 KiB  
Article
Efficient Mutual Authentication Protocol between Hospital Internet of Things Devices Using Probabilistic Attribute Information
by Yoon-Su Jeong, Dong-Ryool Kim and Seung-Soo Shin
Sustainability 2019, 11(24), 7214; https://0-doi-org.brum.beds.ac.uk/10.3390/su11247214 - 16 Dec 2019
Cited by 2 | Viewed by 2338
Abstract
Wearable and portable medical devices are one of the fastest growing sectors in the Internet of Things (IoT) market. However, medical services specialize in the processing of personal health data, which carries issues that are not faced by other industries. In this paper, [...] Read more.
Wearable and portable medical devices are one of the fastest growing sectors in the Internet of Things (IoT) market. However, medical services specialize in the processing of personal health data, which carries issues that are not faced by other industries. In this paper, we propose a multi-dimensional color vector information based IoT device authentication protocol that can provide benefits for medical work, assuming that a hospital has the capability of integrating IoT devices and has access to patient information. The proposed protocol uses multi-dimensional color vectors to help users who use IoT devices to manage their condition in multiple groups, stochastically. In addition, the proposed protocol provides the health and medical service status of users to medical staff in real time using IoT authentication keys generated through the proposed multi-dimensional color vectors. The proposed protocol not only addresses health care problems yet to be tackled in the management of hospital and health services, but also minimizes administrative time and procedures for current medical services. As a result of the performance evaluation, the proposed protocol improved the efficiency of hospital IoT devices by an average of 31.1%, and the time delay for medical services was improved by 19.8%, compared to the existing protocol. By using the proposed protocol and IoT devices, the average overhead of healthcare providers could be reduced by as much as 15.3%. Full article
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
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