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.

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.

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.

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.

Applications of Big Data Analytics in Healthcare Management Systems

2021 ◽  
Vol 23 (06) ◽  
pp. 1167-1182
Author(s):  
Shreyas Nopany ◽  
◽  
Prof. Manonmani S ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Management ◽  
Healthcare Sector ◽  
Future Research ◽  
Healthcare Industry ◽  
Strategy Formulation ◽  
Data Governance ◽  
Number Of Patients

The healthcare industry has become increasingly demanding in recent years. The growing number of patients makes it difficult for doctors and staff to manage their work effectively. In order to achieve their objectives, data analysts collect a large amount of data, analyze it, and use it to derive valuable insights. Data analytics may become a promising solution as healthcare industry demands increase. The paper discusses the challenges of data analytics in the healthcare sector and the benefits of using big data for healthcare analytics. Aside from focusing on the opportunities that big data analytics has in the healthcare sector, the paper will also discuss data governance, strategy formulation, and improvements to IT infrastructure. Implementation techniques include Hadoop, HDFS, MapReduce, and Apache in Big Data Analytics. A Healthcare Management System can be categorized into five divisions, namely, Drug discovery, Disease prevention, diagnosis and treatment, Hospital operations, post-care, requiring comprehensive data management. Big Data analysis support transformation is identified as a required component in future research for the application of Big Data in HealthCare.

Integrated Data Architecture for Business

2019 ◽  
pp. 478-490
Author(s):  
Richard Kumaradjaja
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Point Of View ◽  
Future Research ◽  
Integration Framework ◽  
Architecture Framework ◽  
Data Architecture ◽  
Future Research Directions

This chapter describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this chapter is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better construction of proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The chapter also discusses future research directions of the proposed integrated data architecture framework.

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.

scholarly journals Structurally and Arithmetically Controlled Grammars

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
S Ashaari ◽  
SHERZOD TURAEV ◽  
A Okhunov
Keyword(s):  
Big Data ◽  
Dna Computing ◽  
Data Analytics ◽  
Formal Language ◽  
Big Data Analytics ◽  
Language Theory ◽  
Future Research ◽  
Extensive Literature ◽  
Quarter Century ◽  
Scientific Disciplines

Over the quarter century, it is gratifying to note that the significance of regulated or controlledgrammars (i.e. grammars with regulated rewriting) has been recognized by many parties where it has beenused widely in a great variety of scientific disciplines ranging from Linguistics through DNA Computing upto the Informatics and recently come to Big Data Analytics. Therefore, literally we can find hundreds ofstudies of well-known of various types of controlled grammars and their investigation have amount to athrilling trend within formal language theory. Given the extensive literature on issues related to controlledgrammars, this research focused on arithmetically controlled grammars and tree controlled grammars,which are practically important. Thereby, in this paper, we briefly recapitulate the background of formallanguage theory and highlight the key results of multiset grammars, valence grammars and tree controlledgrammars. In this paper, a new controlled grammar that can be generated using both control mechanismstogether is proposed for future research.

The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Harsh M. Shah ◽  
Bhaskar B. Gardas ◽  
Vaibhav S. Narwane ◽  
Hitansh S. Mehta
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions

PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

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