scholarly journals Music Industry in the Big Data Era: Sociological and Marketing Research

2018 ◽  
Vol 6 (4) ◽  
pp. 161-172
Author(s):  
Marina G. Snezhinskaya
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Music Industry ◽  
Marketing Research ◽  
Professional Competencies ◽  
Data Usage ◽  
Big Data Technologies ◽  
Music Market ◽  
Musical Preferences

The Big Data technologies and the potential of their application in the music industry are reviewed in the article. The main questions raised concern the perspectives of the Big Data usage in the sociological and marketing research, the audience data analysis and the musical preferences of the audience. The Big Data allow to discover new artists and to find new ways of stimulating the audience’s loyalty. The author attempts to answer the question: how does the Big Data change the music industry? The possibility of using the Big Data to forecast the audience’s behavior is being reviewed. The examples of the Big Data technologies usage in the marketing research for the music industry are exposed. The author underlines the importance of this technology for sociologists and market researchers and brings into the light the problems of the Big Data usage. The attention is drawn to the development of the new sphere of musical data science and to the necessity of broadening the professional competencies of sociologists and music market researchers.

Data Science and Big Data Analytics in Financial Services

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

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.

scholarly journals A Microservice-Based Big Data Analysis Platform for Online Educational Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Modular Design ◽  
Science Research ◽  
Big Data Analysis ◽  
Research Field ◽  
Traditional Work ◽  
Educational Applications ◽  
Analysis Platform

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.

scholarly journals Hemi- and Homonyms in the Big Data Era

Diversity ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 472
Author(s):  
Jorge Rubén Sánchez-González
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Biological Monitoring ◽  
Data Science ◽  
General Rule ◽  
Big Data Analysis ◽  
Species List ◽  
Statistical Software ◽  
Independence Principle ◽  
Near Future

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.

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

scholarly journals Enablers to supply chain performance on the basis of digitization technologies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Gupta ◽  
Sarangdhar Kumar ◽  
Simonov Kusi-Sarpong ◽  
Charbel Jose Chiappetta Jabbour ◽  
Martin Agyemang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information Technologies ◽  
Data Science ◽  
Content Type ◽  
Big Data Technologies ◽  
Fierce Competition ◽  
Practical Implications ◽  
Improve Supply Chain

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

Data Science and Big Data Analytics in Financial Services

2016 ◽  
pp. 396-423 ◽  
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

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.

scholarly journals Medical Healthcare Network Platform and Big Data Analysis Based on Integrated ICT and Data Science with Regulatory Science

2019 ◽  
Vol E102.B (6) ◽  
pp. 1078-1087 ◽  
Author(s):  
Ryuji KOHNO ◽  
Takumi KOBAYASHI ◽  
Chika SUGIMOTO ◽  
Yukihiro KINJO ◽  
Matti HÄMÄLÄINEN ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Big Data Analysis ◽  
Regulatory Science ◽  
Healthcare Network ◽  
Network Platform ◽  
Medical Healthcare

scholarly journals An overview of big data analysis

2020 ◽  
Vol 9 (4) ◽  
pp. 1646-1653
Author(s):  
Fabio Arena ◽  
Giovanni Pau
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Service ◽  
Big Data Analytics ◽  
Short Review ◽  
Big Data Analysis ◽  
Digital World ◽  
Big Data Technologies ◽  
Definition Of ◽  
Smart Cars

Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies is happening within banking, healthcare, insurance, securities and investment services, and telecommunications. Remarkably, three of those industries lie within the financial sector, which has many particularly serviceable use cases for big data analytics, such as fraud detection, risk management, and customer service optimization. In fact, the definition of big data analysis refers to the process that encompasses the gathering and analysis of big data to obtain useful information for the business. This paper focuses on delivering a short review concerning the current technologies, future perspectives, and the evaluation of some use cased associated with the analysis of big data.

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