Analysis of Semantic Network of Leisure Before and After Covid-19 Using Media Big Data

2021 ◽  
Vol 60 (2) ◽  
pp. 163-176
Author(s):  
Kyung Sik Kim
Keyword(s):  
Big Data ◽  
Semantic Network ◽  
Before And After

Evaluation of Ship Performance Using Operation Records

2018 ◽  
Author(s):  
Kenichi Fukuda ◽  
Yoshihisa Okada ◽  
Akinori Okazaki ◽  
Hiroyuki Adachi ◽  
Yuichiro Hisamuara ◽  
...  
Keyword(s):  
Big Data ◽  
Fuel Oil ◽  
Time Dependent ◽  
Oil Consumption ◽  
Sea Trial ◽  
Before And After ◽  
Report Data ◽  
Unique Approach ◽  
Performance Conditions ◽  
Ship Performance

Recently, the big data can be employed as the economical ship operating or evaluation of ship performance conditions. However, such data cannot be easily obtained and analyzed for every ship. In this case, for example, an evaluation of ship performance during operation is usually dependent on ship owner’s experience. The time-dependent ship performance is an essential topic for ship owners because if they realize their current ship performance, they can implement something such as hull or propeller cleaning for their economical operation. This study is focused on the usage of noon report data rather than the big data due to their obtainability. Usually, such data are considered as references because different ship operational condition and environmental condition obscure current ship performance. However, our unique approach, which is used integrally the noon report data such as BHP, propeller revolution and fuel oil consumption, ship sea trial data and propeller performance, can be evaluated ship performance during ship in service. The analyzed output data can be produced as increasing of ship resistance (delta Rw) versus ship performance efficiency, fuel oil consumption (ton per day) or sea margin. Under this output conditions, it can be comparable at same conditions even though the conditions of operations are different. Therefore, this analyzed data has a potential ability to have a look at ship performance conditions during ship in service. The purpose of this paper is to introduce our unique approach using noon data for time-dependent ship performance and then discuss the verification of this approach. As the case study, the noon report data for Japanese domestic bulker was chosen and the ship performance was evaluated in terms of different points of views. It was done comparing the conditions of before and after dry dock to evaluate our approach. In addition, the potential application of this approach will be discussed in this paper.

Big data analysis of trends in the second-hand apparel market: a comparison between 2014 and 2019

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanghee Kim ◽  
Hongjoo Woo
Keyword(s):  
Big Data ◽  
Design Methodology ◽  
Semantic Network ◽  
Distribution Channels ◽  
Subject Area ◽  
Future Research ◽  
Semantic Network Analysis ◽  
Content Type ◽  
The Past ◽  
Network Patterns

Purpose According to the perspective of evolutionary economic theory, the marketplace continuously evolves over time, following the changing needs of both customers and firms. In accordance with the theory, the second-hand apparel market has been rapidly expanding by meeting consumers’ diverse preferences and promoting sustainability since 2014. To understand what changes in consumers’ consumption behaviors regarding used apparel have driven this growth, the purpose of this study is to examine how the second-hand apparel market product types, distribution channels and consumers’ motives have changed over the past five years. Design/methodology/approach This study collected big data from Google through Textom software by extracting all Web-exposed text in 2014, and again in 2019, that contained the keyword “second-hand apparel,” and used the Node XL program to visualize the network patterns of these words through the semantic network analysis. Findings The results indicate that the second-hand apparel market has evolved with various changes over the past five years in terms of consumer motives, product types and distribution channels. Originality/value This study provides a comprehensive understanding of the changing demands of consumers toward used apparel over the past five years, providing insights for retailers as well as future research in this subject area.

scholarly journals Uncovering Trend-based Research Insights on Teaching & Learning in Big Data

2020 ◽  
Author(s):  
Young-Eun Park
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Teaching And Learning ◽  
Learning Algorithm ◽  
Semantic Network ◽  
Academic Leaders ◽  
Student Centered ◽  
Diverse Learning ◽  
Quality Programs ◽  
Future Education

Abstract Along with the occurrence of the big data era, digital transformation has had a transformative effect on modern education tremendously in higher education. It transforms an institutional core value of education to better meet students' needs by leveraging big data and digital technology. Based on this background, this study attempts to catch the principal trends, or new directions, paradigms as predictors with an association of each topic by discovering the up-to-date research trends on teaching and learning in higher education via text mining technique. For this, 285 research articles in the area of teaching and learning in higher education were collected from several big databases (distinguishable publishers' web platforms) through search engines for two years in 2018 ~ 2019. Then it was analyzed using a semantic network analysis that processes natural human language. In consequence, research results show a relatively high connection with 'student' or 'student-centered/led' rather than 'teacher-led.' Moreover, it exhibits that the practice and assessment in learning can be attained via diverse learning activities, containing community or outreach activities. Besides, research in academic contexts, experience-based classes, the effect of group activities, how students' feelings or perceptions, and relationships affect learning outcomes were addressed as the main topics through topic modeling of LDA, a machine learning algorithm. This study proposes that educators, researchers, and even academic leaders can exert the extraordinary power to reshape educational quality programs for future education and in a timely manner with recognizable trends or agendas in teaching and learning of higher education.

The Strategic Value of Big Data Analytics in Health Care Policy-Making

2018 ◽  
Vol 14 (3) ◽  
pp. 20-33 ◽  
Author(s):  
Hamed M. Zolbanin ◽  
Dursun Delen ◽  
Sushil K Sharma
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Health Care Policy ◽  
Big Data Analytics ◽  
Advantages And Disadvantages ◽  
Strategic Value ◽  
Before And After ◽  
The Government ◽  
New Policies

This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymakers to obtain a better understanding of loopholes and to propose more effective policies in prospective endeavors.

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