Big Data Analytics

2017 ◽  
pp. 13-23
Author(s):  
Nachiappan Subramanian ◽  
Muhammad D. Abdulrahman ◽  
Hing Kai Chan ◽  
Kun Ning
Keyword(s):  
Big Data ◽  
Growth And Development ◽  
Big Data Analytics ◽  
Computer Hardware ◽  
Future Research ◽  
Strategic Development ◽  
Research Directions ◽  
Implementation Challenges ◽  
Big Picture ◽  
Future Research Directions

In this chapter, we will introduce practical issues and implementation challenges from the industry perspective. In particular, we explain three aspects based on the panel discussions from the set of representatives participated in a big data conference from three dominant industries such as e-commerce, health care and computer hardware, which are sought of big data for their growth and development. We introduce overall challenges and explain typical industry based practical issues, how they visualize the big picture for their strategic development and how industries are gearing towards converting the challenges to big opportunities through the partnership of universities. Finally, based on the content analysis we offer potential trends and future research directions.

Big Data Analytics Driven Supply Chain Transformation

2019 ◽  
pp. 106-124 ◽  
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

Big Data Analytics Driven Supply Chain Transformation

2022 ◽  
pp. 1413-1432
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2018 ◽  
pp. 81-116
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

scholarly journals Melatonin in plants: what we know and what we don’t

2021 ◽  
Vol 5 ◽  
Author(s):  
Zixin Zhang ◽  
Yang Zhang
Keyword(s):  
Abiotic Stresses ◽  
Growth And Development ◽  
Current Knowledge ◽  
Future Research ◽  
Signal Transduction Pathways ◽  
Biotic And Abiotic Stresses ◽  
Research Directions ◽  
Genetic Characteristics ◽  
Future Research Directions ◽  
Biosynthesis Regulation

Abstract Melatonin is an endogenous micromolecular compound of indoleamine with multiple physiological functions in various organisms. In plants, melatonin is involved in growth and development, as well as in responses to biotic and abiotic stresses. Furthermore, melatonin functions in phytohormone-mediated signal transduction pathways. There are multiple melatonin biosynthesis pathways, and the melatonin content in plants is greatly affected by intrinsic genetic characteristics and external environmental factors. Although melatonin biosynthesis has been extensively studied in model plants, it remains uncharacterized in most plants. This article focuses on current knowledge on the biosynthesis, regulation and application of melatonin, particularly for fruit quality and preservation. In addition, it highlights the links between melatonin and other hormones, as well as future research directions.

Future Research Directions in E-Tourism Studies

Big Data ◽  
2016 ◽  
pp. 2368-2387
Author(s):  
Hajime Eto
Keyword(s):  
Big Data ◽  
Future Research ◽  
Research Directions ◽  
Scientific Methods ◽  
Tourism Studies ◽  
The Future ◽  
Future Research Directions ◽  
And Behaviors ◽  
Public Voice

As this book has the limited numbers of chapters and pages, many important issues remain unanalyzed. This chapter picks up and roughly discusses some of them for the future analyses in more analytical ways. The focuses are placed on how to apply the data scientific methods to the analyses of public voice, claims and behaviors of tourists, customers and the general publics by using the big data already acquired and stored somewhere.

On the Road to Ephesus

2015 ◽  
pp. 102-125
Author(s):  
Suzanne Roff-Wexler
Keyword(s):  
Big Data ◽  
Future Research ◽  
Review Of Literature ◽  
Healthcare Organizations ◽  
Research Directions ◽  
The Road ◽  
Future Research Directions ◽  
On The Road ◽  
Definition Of

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.

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