scholarly journals A Keyword Analysis Study on Postpartum Obesity Using Big Data

2021 ◽  
Vol 18 (16) ◽  
pp. 8807
Author(s):  
Hyung-ui Baik ◽  
Bo-Kyung Seo ◽  
Gyu-Ri Kim ◽  
Jung-Eun Ku
Keyword(s):  
Big Data ◽  
Maternal Health ◽  
Academic Research ◽  
Basic Data ◽  
Analysis Study ◽  
Portal Site ◽  
Keyword Analysis ◽  
Mental Problems ◽  
N Gram ◽  
Internet Portals

This study selected Google and Naver, the most recognizable Internet portals in Korea, as subjects for analysis. “Postpartum obesity” and “postpartum depression” were used as keywords for data collection. This study aimed to provide basic data for solving maternal problems using big data. Keywords related to postpartum obesity were collected from the portal site Google from 1 January 2019 to 31 December 2019. The collected data were analyzed through simple frequency analysis, N-gram analysis, and keyword network. This study can be used as basic data for postpartum obesity-related programs or academic research. It is also expected to be used for research on the development of a mobile-based customized healthcare system focused on maternal health. Previous papers and data are still insufficient at solving the physical and mental problems related to postpartum obesity and depression. It is necessary to find ways to continuously integrate and collect data from mothers across the country.

Procurement Analytics: Enabling Data Architectures in the Age of Big Data

2020 ◽  
Author(s):  
Michael Goul ◽  
T. S. Raghu ◽  
Ziru Li
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Research Literature ◽  
Structured Interviews ◽  
Data Space ◽  
Prescriptive Analytics ◽  
Data Architecture ◽  
The Future ◽  
High Level ◽  
Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

scholarly journals Quality of Open Research Data: Values, Convergences and Governance

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 175 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Academic Research ◽  
Research Data ◽  
Data Governance ◽  
Quality Of Data ◽  
Open Research ◽  
Research Environments

This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.

scholarly journals Refocusing loyalty programs in the era of big data: a societal lens paradigm

2020 ◽  
Vol 31 (4) ◽  
pp. 405-418
Author(s):  
Valeria Stourm ◽  
Scott A. Neslin ◽  
Eric T. Bradlow ◽  
Els Breugelmans ◽  
So Yeon Chun ◽  
...  
Keyword(s):  
Big Data ◽  
Technological Change ◽  
Academic Research ◽  
Loyalty Programs ◽  
Societal Factors ◽  
Societal Needs ◽  
Challenges And Opportunities

AbstractBig data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens. We focus on three aspects of the societal lens—inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact each of these societal factors, and then illustrate how, by adopting this societal lens paradigm, researchers and practitioners can generate insights and ideas that address the challenges and opportunities that arise from the interaction between loyalty programs and society. Our goal is to broaden the perspectives of researchers and managers so they can enhance loyalty programs to address evolving societal needs.

Examining the adoption of big data and analytics curriculum

2017 ◽  
Vol 23 (3) ◽  
pp. 506-517 ◽  
Author(s):  
Alexander J. McLeod ◽  
Michael Bliemel ◽  
Nancy Jones
Keyword(s):  
Big Data ◽  
Design Methodology ◽  
Academic Research ◽  
Software Support ◽  
Content Type ◽  
Software Vendor ◽  
Development Agenda ◽  
Practical Information ◽  
Information Materials ◽  
Practical Implications

Purpose The purpose of this paper is to explore the demand for big data and analytics curriculum, provide an overview of the curriculum available from the SAP University Alliances program, examine the evolving usage of such curriculum, and suggest an academic research agenda for this topic. Design/methodology/approach In this work, the authors reviewed recent academic utilization of big data and analytics curriculum in a large faculty-driven university program by examining school hosting request logs over a four-year period. The authors analyze curricula usage to determine how changes in big data and analytics are being introduced to academia. Findings Results indicate that there is a substantial shift toward curriculum focusing on big data and analytics. Research limitations/implications Because this research only considered data from one proprietary software vendor, the scope of this project is limited and may not generalize to other university software support programs. Practical implications Faculty interested in creating or furthering their business process programs to include big data and analytics will find practical information, materials, suggestions, as well as a research and curriculum development agenda. Originality/value Faculty interested in creating or furthering their programs to include big data and analytics will find practical information, materials, suggestions, and a research and curricula agenda.

scholarly journals A Corpus-based Study of Japanese Verb Paradigms (Preliminary results)

10.29007/tvck ◽  
2019 ◽  
Author(s):  
Anna Novoselova ◽  
Alexander Kostyrkin
Keyword(s):  
Big Data ◽  
Written Language ◽  
Japanese Language ◽  
Preliminary Results ◽  
Verb Forms ◽  
N Gram ◽  
Language Description

The Japanese language has a great variety of verb inflectional suffixes (auxiliaries), each having conjugation of their own. In this paper we propose a corpus-based approach to studying Japanese verb paradigms. Such an approach benefits from identifying possible verb forms on big data of written language. Description of methods and tools used for building databases of verbs and auxiliaries and for parsing verb 7-grams from a Japanese N-gram Corpus is presented.

scholarly journals Can Big Data Analysis Make Cities Smarter? Emerging Issues and Stakeholder Implications in Smart Mobility

2017 ◽  
Vol 8 (1) ◽  
pp. 23 ◽  
Author(s):  
Asst. Prof. Dr. Serkan Gürsoy ◽  
Asst. Prof. Dr. Murat Yücelen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Smart Cities ◽  
Academic Research ◽  
Big Data Analysis ◽  
Ongoing Debate ◽  
Research Fields ◽  
Emerging Issues ◽  
Smart Mobility ◽  
Applied Fields

This study deals with the challenges and bottlenecks with respect to the concept of smart cities which has largely been constructed on knowledge utilization issues and challenges. Despite the abundant existent literature in this field, the effective transformation of data into knowledge which can become a source of competitive advantage is still an ongoing debate, especially due to contemporary developments in big data analysis methods, approaches and strategies. As an emerging problem, the derivation of significant meaning from big data is among popular academic research fields, as well as being a crucial industrial and policy making engagement regarding value creating mechanisms in smart cities. Therefore in this study, limitations and challenges in translating big data into valuable knowledge in academia and industries are considered within the concept of smart mobility. In an attempt to propose researchers, business firms and governmental entities a collaborative approach, a perception about emerging issues is presented for clarifying some future constructs intersecting in relevant research and applied fields.

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