scholarly journals Big Data, Biomedical Research, and Ethics Review: New Challenges for IRBs

2020 ◽  
Vol 42 (5) ◽  
pp. 17-28
Author(s):  
Agata Ferretti ◽  
Marcello Ienca ◽  
Samia Hurst ◽  
Effy Vayena
Keyword(s):  
Big Data ◽  
Biomedical Research ◽  
Ethics Review ◽  
New Challenges

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

scholarly journals Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research

2018 ◽  
Vol 123 (12) ◽  
pp. 1282-1284 ◽  
Author(s):  
Fatima Rodriguez ◽  
David Scheinker ◽  
Robert A. Harrington
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Biomedical Research ◽  
Clinical Medicine

scholarly journals Agile Elastic Desktop Corporate Architecture for Big Data

2020 ◽  
Vol 20 (3) ◽  
pp. 15-31
Author(s):  
Valentin Kisimov ◽  
Dorina Kabakchieva ◽  
Aleksandar Naydenov ◽  
Kamelia Stefanova
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Business Environment ◽  
Digital Transformation ◽  
Big Data Processing ◽  
Online Business ◽  
New Challenges ◽  
Corporate Architecture ◽  
Effective Use ◽  
Corporate Business

AbstractNew challenges in the dynamically changing business environment require companies to experience digital transformation and more effective use of Big Data generated in their expanding online business activities. A possible solution for solving real business problems concerning Big Data resources is proposed in this paper. The defined Agile Elastic Desktop Corporate Architecture for Big Data is based on virtualizing the unused desktop resources and organizing them in order to serve the needs of Big Data processing, thus saving resources needed for additional infrastructure in an organization. The specific corporate business needs are analyzed within the developed R&D environment and, based on that, the unused desktop resources are customized and configured into required Big Data tools. The R&D environment of the proposed Agile Elastic Desktop Corporate Architecture for Big Data could be implemented on the available unused resources of hundreds desktops.

The Novel Multi-layered Approach to Enhance Sorting Performance of Healthcare Analysis

2022 ◽  
Vol 11 (3) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Data ◽  
The Novel ◽  
Analytical Technique ◽  
Huge Data ◽  
Costs Of Treatment ◽  
Layered Approach ◽  
New Challenges

Emergence of big data in today’s world leads to new challenges for sorting strategies to analyze the data in a better way. For most of the analyzing technique, sorting is considered as an implicit attribute of the technique used. The availability of huge data has changed the way data is analyzed across industries. Healthcare is one of the notable areas where data analytics is making big changes. An efficient analysis has the potential to reduce costs of treatment and improve the quality of life in general. Healthcare industries are collecting massive amounts of data and look for the best strategies to use these numbers. This research proposes a novel non-comparison based approach to sort a large data that can further be utilized by any big data analytical technique for various analyses.

Big Data Literacy

Big Data ◽  
2016 ◽  
pp. 2300-2315
Author(s):  
Dimitar Christozov ◽  
Stefka Toleva-Stoimenova
Keyword(s):  
Big Data ◽  
Digital Divide ◽  
Computer Technology ◽  
Human Society ◽  
Data Literacy ◽  
Learning From Data ◽  
New Challenges

The chapter addresses the problems of digital divide in the light of learning from data in the era of accumulated, available, and accessible Big Data. The phenomenon of Big Data arose in the last years and offered new dimensions of digital divide: the challenge that the human society faces since the appearance of computer technology. Objectives of this chapter are to highlight problems and barriers in learning from Big Data and to initiate discussion on the ways to overcome those new challenges. The chapter tries to define the “Big Data Phenomenon,” to identify the phases and activities in the process of learning from data, and to relate them to learning from Big Data. As a result, a paradigm of competences and barriers for acquiring Big Data literacy are proposed as a new dimension of literacy in dividing the human society.

Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

2022 ◽  
pp. 187-204
Author(s):  
María A. Pérez-Juárez ◽  
Javier M. Aguiar-Pérez ◽  
Miguel Alonso-Felipe ◽  
Javier Del-Pozo-Velázquez ◽  
Saúl Rozada-Raneros ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Financial Institutions ◽  
Financial Services ◽  
Essential Role ◽  
Digital Natives ◽  
Digital Immigrants ◽  
Artificial Intelligence Techniques ◽  
New Challenges

A lot of millennials have been educated in gamified schools where they played Kahoot several times per week, and where applications like Classcraft made them feel like the protagonists of a videogame in which they had to accumulate points to be able to level up. All those that were educated in a gamified environment feel it is natural and logical that gamification is used in all areas. For this reason, gamification is increasingly becoming important in different fields including financial services, bringing new challenges. Gamification allows financial institutions to provide personalized and compelling experiences. Big data and artificial intelligence techniques are called to play an essential role in the gamification of financial services. This chapter aims to explore the possibilities of using artificial intelligence and big data techniques to support gamified financial services which are essential for digital natives but also increasingly important for digital immigrants.

Mobile Big Data

2016 ◽  
pp. 138-158
Author(s):  
Shivom Aggarwal ◽  
Abhishek Nayak
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Value Chain ◽  
Mobile Technologies ◽  
Complex Concept ◽  
Current Trends ◽  
Mobile Big Data ◽  
Challenges And Opportunities ◽  
New Challenges ◽  
Data Ecosystem

Mobile technologies have given rise to tremendous amounts of data in real-time, which can be unstructured and uncertain. This growth can be attributed as Mobile Big Data and provides new challenges and opportunities for innovation. This chapter attempts to define the concept of Mobile Big Data, provide description of various sources of Mobile Big Data and discuss SWAI (Sources Warehousing Analytics Insights) model of Big Data processing. To understand this complex concept, it is important to visualize the Big Data ecosystem, respective players. Moreover, mobile computing, Internet of things, and other associated technologies have been discussed in light of marketing and communications based applications. The current trends in Mobile Big Data and associated value chain help us understand where the next frontiers of innovation are and how one can create value. This is linked to the future aspects of the Mobile Big Data and evolution of technologies from now onwards.

8. Intelligence and Strategy

2015 ◽  
Author(s):  
Roger Z. George
Keyword(s):  
Big Data ◽  
Cold War ◽  
Pearl Harbor ◽  
Cuban Missile Crisis ◽  
Yom Kippur War ◽  
Data Problem ◽  
War In Iraq ◽  
New Challenges ◽  
Yom Kippur

This chapter explores the role of intelligence in strategy. It first explains what intelligence is and how strategists have talked about its utility before discussing the development of U.S. intelligence in its early efforts to support cold war strategies of containment and deterrence and in its more recent support to strategies for counterterrorism and counterinsurgency. It then examines the challenges and causes of ‘strategic surprise’, focusing on the historical cases of Pearl Harbor in 1941, the Cuban Missile Crisis in 1962, the Yom Kippur War in 1973, and the 11 September 2001 attacks. It also describes some of the new challenges faced by intelligence after a decade of war in Iraq and Afghanistan as well as in dealing with the new ‘big data’ problem.

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