scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

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.

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.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals Real-Time Supply Chain Simulation: A Big Data-Driven Approach

2019 ◽  
Author(s):  
Antonio A.C. Vieira ◽  
Luis M.S. Dias ◽  
Maribel Y. Santos ◽  
Guilherme A.B. Pereira ◽  
Jose A. Oliveira
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Real Time ◽  
Data Driven ◽  
Data Driven Approach ◽  
Supply Chain Simulation

scholarly journals Personnel management in the sustainable transport and logistics industry

2021 ◽  
Vol 128 ◽  
pp. 04022
Author(s):  
Katarína Hercegová ◽  
Alexander Pyanov ◽  
Oksana Mukhoryanova
Keyword(s):  
Human Resource ◽  
Data Driven ◽  
Human Resource Managers ◽  
Sustainable Transport ◽  
Comprehensive Overview ◽  
Logistics Industry ◽  
Resource Managers ◽  
Transport Industry ◽  
Data Driven Approach ◽  
The Right

This paper describes the methods and techniques of the ways personnel security is ensured and maintained in the sustainable transport industry. In addition, it focuses on the novel methods and technologies used by the human resource managers for selecting and hiring candidates for jobs in the transport and logistic sector. Furthermore, it gives a comprehensive overview of human capital management in the transport industry and provides a detailed analysis of several segments covered. It offers a detailed insight into the growth markets and their impact on the human resource management market in the transport industry. Our results demonstrate that the majority of the world's largest transportation and logistics companies believe that data-driven decision-making is essential to supply chain activities and is hiring the right employees. The paper shows that this data-driven approach might be the best solution for optimizing performance and achieving the standards of sustainable and environmentally-friendly business both at the personnel level and at the level of operation and efficient management. Moreover, it stresses the importance of the artificial intelligence and deep learning in the development of the sustainable transport industry.

scholarly journals Big Data-Driven Approach for Health Inequalities in Foreign Patients with Injuries Visiting Emergency Rooms

2020 ◽  
Vol 26 (1) ◽  
pp. 34
Author(s):  
Jin Young Kang ◽  
Jinhee Kwon ◽  
Chang Hwan Sohn ◽  
Youn-Jung Kim ◽  
Hyo Won Lim ◽  
...  
Keyword(s):  
Big Data ◽  
Health Inequalities ◽  
Data Driven ◽  
Emergency Rooms ◽  
Data Driven Approach

scholarly journals Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

2016 ◽  
Vol 20 (1) ◽  
pp. 13-28 ◽  
Author(s):  
David H. Olsen ◽  
Pamela A. Dupin-Bryant
Keyword(s):  
Big Data ◽  
Popular Culture ◽  
Learning Outcomes ◽  
Data Science ◽  
Data Driven ◽  
Job Market ◽  
Data Cleansing ◽  
Problem Solving Skills ◽  
5E Instructional Model ◽  
Data Market

Big data and data science have experienced unprecedented growth in recent years.  The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial.  Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses.

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