scholarly journals Hemi- and Homonyms in the Big Data Era

Diversity ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 472
Author(s):  
Jorge Rubén Sánchez-González

The issue of hemi- and homonyms is an unsolved topic in the Big Data era, where informatics and technicians, rather than biologists or taxonomists, analyze huge datasets. Nowadays, taxonomic nomenclature is ruled by four independent international codes, and according to them, the existence of hemihomonyms and homonyms is accepted under some conditions as an exception to the general rule. This situation entails confusion, disagreements, and a plethora of problems whose consequences could worsen in the near future within the framework of the big data era. Moreover, the increasing use of big databases and analyses, data science, bioinformatics, biological monitoring, and bioassessment has shown such exceptions to be inconvenient, since these exceptions to homonyms are considered as duplicates by databases and statistical software, which are handled by non-taxonomist experts. International Codes of Nomenclature must change within the new context of big data analysis. This work aims to propose the elimination of any exception to the presence of homonyms and to evaluate whether the Independence Principle makes sense within this new context. Increasing coordination between several independent nomenclatural systems is essential and, perhaps, we must conduct our efforts towards a universal species list, finishing with the historical schism between Codes.

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.


Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.


Author(s):  
Busayasachee Puang-Ngern ◽  
Ayse A. Bilgin ◽  
Timothy J. Kyng

There is currently a shortage of graduates with the necessary skills for jobs in data analytics and “Big Data”. Recently many new university degrees have been created to address the skills gap, but they are mostly computer science based with little coverage of statistics. In this chapter, the perceptions of graduates and academics about the types of expertise and the types of software skills required for this field are documented based on two online surveys in Australia and New Zealand. The results showed that Statistical Analysis and Statistical Software Skills were the most necessary type of expertise required. Graduates in industry identified SQL as the most necessary software skill while academics teaching in relevant disciplines identified R programming as the most necessary software skill for Big Data analysis. The authors recommend multidisciplinary degrees where the appropriate combination of skills in statistics and computing can be provided for future graduates.


2019 ◽  
Vol E102.B (6) ◽  
pp. 1078-1087 ◽  
Author(s):  
Ryuji KOHNO ◽  
Takumi KOBAYASHI ◽  
Chika SUGIMOTO ◽  
Yukihiro KINJO ◽  
Matti HÄMÄLÄINEN ◽  
...  

Web Services ◽  
2019 ◽  
pp. 1166-1188
Author(s):  
Busayasachee Puang-Ngern ◽  
Ayse A. Bilgin ◽  
Timothy J. Kyng

There is currently a shortage of graduates with the necessary skills for jobs in data analytics and “Big Data”. Recently many new university degrees have been created to address the skills gap, but they are mostly computer science based with little coverage of statistics. In this chapter, the perceptions of graduates and academics about the types of expertise and the types of software skills required for this field are documented based on two online surveys in Australia and New Zealand. The results showed that Statistical Analysis and Statistical Software Skills were the most necessary type of expertise required. Graduates in industry identified SQL as the most necessary software skill while academics teaching in relevant disciplines identified R programming as the most necessary software skill for Big Data analysis. The authors recommend multidisciplinary degrees where the appropriate combination of skills in statistics and computing can be provided for future graduates.


2019 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Elham Nazari ◽  
Marziyeh Afkanpour ◽  
Hamed Tabesh

The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.


2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel

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