Fungal biodiversity and conservation mycology in light of new technology, big data, and changing attitudes

2021 ◽  
Vol 31 (19) ◽  
pp. R1312-R1325
Author(s):  
Lotus A. Lofgren ◽  
Jason E. Stajich
Keyword(s):  
Big Data ◽  
New Technology ◽  
Fungal Biodiversity ◽  
Changing Attitudes

scholarly journals BIG DATA FOR RISK ANALYSIS: THE FUTURE OF SAFE RAILWAYS

2016 ◽  
Author(s):  
Miguel Figueres Esteban
Keyword(s):  
Big Data ◽  
Risk Analysis ◽  
Safety Management ◽  
New Technology ◽  
Data Sources ◽  
Transport Systems ◽  
Text Documents ◽  
Railway Systems ◽  
Wide Range ◽  
Three Components

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1825

The impact of big data on innovation and value generation in pharmaceutical sales and marketing

2021 ◽  
Vol 3 (2) ◽  
pp. 37-52
Author(s):  
Antonio Pesqueira
Keyword(s):  
Big Data ◽  
New Technology ◽  
Unmet Need ◽  
Pharmaceutical Companies ◽  
Future Trends ◽  
Pharmaceutical Sales ◽  
Sales And Marketing ◽  
Investment Opportunities ◽  
Application Fields ◽  
The Impact

Using Big Data in the pharmaceutical industry is a relatively new technology, and the benefits and applications are yet to be understood. There are some cases currently being piloted, but others have already been adopted by some pharmaceutical organizations, proving the unmet need in a field that is still in its infancy. This paper aims to understand how and if Big Data can contribute to commercial innovation, as well as future trends, investment opportunities. Participants from 26 pharmaceutical companies participated in different focus groups where topics were grouped by individuals and evaluation areas were discussed to discover any potential connections between Big Data and Innovation in commercial pharmaceutical environments. This study used the collected data to analyze and draw conclusions about how many life sciences leaders and professionals already know about Big Data and are identifying examples and processes where Big data is supporting and generating innovation. In addition, we were able to understand that the industry is already comfortable with Big Data, and there were some very accurate research results regarding the most pertinent application fields and key considerations moving forward. Using the network analysis findings and the relationships and connections explained by respondents, we can reveal how Big Data and innovation are interconnected.

A Discussion on recent advances in heavy electrical plant - Superconducting machines - a new era for the electrical power industry

1973 ◽  
Vol 275 (1248) ◽  
pp. 77-84 ◽  
Keyword(s):  
New Technology ◽  
Electrical Power ◽  
Industrial Applications ◽  
Propulsion Systems ◽  
Successful Development ◽  
New Era ◽  
Marine Propulsion ◽  
Short Period ◽  
Under Construction ◽  
Changing Attitudes

This paper outlines the development of superconducting d.c. machines at I.R.D. where most of the work to date has been undertaken. Particular emphasis will be placed upon the industrial applications for these machines and the paper contains illustrations of the superconducting marine propulsion systems now under construction. The object of the presentation is to demonstrate that superconducting d.c. machines are now available for industrial application after a relatively short period of development. The paper also indicates the substantial advantages to be gained from the successful development of superconducting a.c. generators. The work which is necessary before these machines may be put into production will be discussed by consideration of the principal problem areas. Finally, conclusions are drawn on the present status of superconducting machines and the changing attitudes in industry towards this new technology.

Applications of Big Data in Large and Small Systems

2021 ◽  
pp. 20-37
Author(s):  
Archana Purwar ◽  
Indu Chawla
Keyword(s):  
Big Data ◽  
Soft Computing ◽  
New Technology ◽  
Electronic Devices ◽  
Small Systems ◽  
Innovative Methods ◽  
Research Challenges ◽  
Basic Understanding ◽  
Basic Concepts ◽  
Tools And Techniques

Nowadays, big data is available in every field due to the advent of computers and electronic devices and the advancement of technology. However, analysis of this data requires new technology as the earlier designed traditional tools and techniques are not sufficient. There is an urgent need for innovative methods and technologies to resolve issues and challenges. Soft computing approaches have proved successful in handling voluminous data and generating solutions for them. This chapter focuses on basic concepts of big data along with the fundamental of various soft computing approaches that give a basic understanding of three major soft computing paradigms to students. It further gives a combination of these approaches namely hybrid soft computing approaches. Moreover, it also poses different applications dealing with big data where soft computing approaches are being successfully used. Further, it comes out with research challenges faced by the community of researchers.

scholarly journals Communicable Disease Surveillance Ethics in the Age of Big Data and New Technology

2019 ◽  
Vol 11 (2) ◽  
pp. 173-187 ◽  
Author(s):  
Gwendolyn L. Gilbert ◽  
Chris Degeling ◽  
Jane Johnson
Keyword(s):  
Big Data ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
New Technology

A Critical Review of the Big-Data Paradigm

2016 ◽  
Vol 5 (3) ◽  
pp. 46-59
Author(s):  
Ruben Xing ◽  
Jinluan Ren ◽  
Jianghua Sun ◽  
Lihua Liu
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Internet Of Things ◽  
Social Science ◽  
Critical Review ◽  
New Technology ◽  
Business Leaders ◽  
Transitional Period ◽  
Strategic Plans ◽  
Strategic Changes

The moving directions of big data are readjusted with updated concerns along with the quick boom of Internet of Things (IoT). Any serious contribution to the advance of the IoT must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. Big data was a hot topic in past years. It is not a new technology, but a huge resource generated from those fields. Some of the omitted focuses become major strategic plans for developers, and several new functions are becoming critical needs for the smart Internet movement. This paper is to address big data with the strategic changes and directions during the sensitive transitional period to be recognized for the business leaders and information technology (IT) developers.

Predictable policing: New technology, old bias, and future resistance in big data surveillance

2020 ◽  
Vol 26 (5-6) ◽  
pp. 1108-1124
Author(s):  
Xerxes Minocher ◽  
Caelyn Randall
Keyword(s):  
Big Data ◽  
Science And Technology Studies ◽  
New Technology ◽  
The United States ◽  
Surveillance Network ◽  
Predictive Policing ◽  
Sociotechnical System ◽  
Power And Control ◽  
Data Surveillance ◽  
And Control

Within this article, we explore the rise of predictive policing in the United States as a form of big data surveillance. Bringing together literature from communication, criminology, and science and technology studies, we use a case study of Milwaukee, Wisconsin, USA to outline that predictive policing, rather than being a novel development, is in fact part of a much larger, historical network of power and control. By examining the mechanics of these policing practices: the data inputs, behavioral outputs, as well as the key controllers of these systems, and the individuals who influenced their adoption, we show that predictive policing as a form of big data surveillance is a sociotechnical system that is wholly human-constructed, biases and all. Identifying these elements of the surveillance network then allows us to turn our attention to the resistive practices of communities who historically and presently live under surveillance – pointing to the types of actions and imaginaries required to combat the myth and allure that swirls around the rhetoric of big data surveillance today.

A Study on Big Data New Technology Trends and Market Prospects

2016 ◽  
Vol 22 (11) ◽  
pp. 3563-3566
Author(s):  
Kim Hye-Sun ◽  
Song Ho-Bin ◽  
Lee Jong-Suk
Keyword(s):  
Big Data ◽  
New Technology ◽  
Technology Trends

scholarly journals A Big Data Framework for Quality Assurance and Validation

2019 ◽  
Vol 8 (2) ◽  
pp. 2490-2494
Keyword(s):  
Health Care ◽  
Big Data ◽  
Relational Databases ◽  
Programming Model ◽  
New Technology ◽  
Large Data ◽  
Quality Data ◽  
Post Processing ◽  
Data Framework ◽  
User Data

Big data is a new technology, which is defined by large amount of data, so it is possible to extract value from the capturing and analysis process. Large data faced many challenges due to various features such as volume, speed, variation, value, complexity and performance. Many organizations face challenges while facing test strategies for structured and unstructured data validation, establishing a proper testing environment, working with non relational databases and maintaining functional testing. These challenges have low quality data in production, delay in execution and increase in cost. Reduce the map for data intensive business and scientific applications Provides parallel and scalable programming model. To get the performance of big data applications, defined as response time, maximum online user data capacity size, and a certain maximum processing capacity. In proposed, to test the health care big data . In health care data contains text file, image file, audio file and video file. To test the big data document, by using two concepts such as big data preprocessing testing and post processing testing. To classify the data from unstructured format to structured format using SVM algorithm. In preprocessing testing test all the data, for the purpose data accuracy. In preprocessing testing such as file size testing, file extension testing and de-duplication testing. In Post Processing to implement the map reduce concept for the use of easily to fetch the data.

scholarly journals A Survey on Intermediate Data Management for Big Data and Internet of Things

2018 ◽  
Vol 7 (4.37) ◽  
pp. 86
Author(s):  
Marwah Nihad ◽  
Alaa Hassan ◽  
Nadia Ibrahim
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Management ◽  
New Technology ◽  
Future Internet ◽  
The Internet ◽  
Embedded Devices ◽  
Technology Generation ◽  
Intermediate Data ◽  
The Internet Of Things

The field internet of things and Big Data has become a necessity in our everyday lives due to the broadening of its technology and the exponential increase in devices, services, and applications that drive different types of data. This survey shows the study of Internet of Things (IoT), Big Data, data management, and intermediate data. The survey discusses intermediate data on Big Data and Internet of Things (IoT) and how it is managed. Internet of Things (IoT) is an essential concept of a new technology generation. It is a vision that allows the embedded devices or sensors to be interconnected over the Internet. The future Internet of Things (IoT) will be greatly presented by the massive quantity of heterogeneous networked embedded devices that generate intensively "Big data". Referring to the term intermediate data as the information that is provoked as output data along the process. However, this data is temporary and is erased as soon as you run a model or a sample tool. Also, the existence of intermediate data in both of the Internet of Things (IoT) and Big Data are explained. Here, various aspects of the internet of things, Big Data, intermediate data and data management will be reviewed. Moreover, the schemes for managing this data and its framework are discussed.  

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