Data Science and Business Intelligence Techniques for Learning from Environmental Accident Analysis for Offshore Oil Fields

2019 ◽  
Author(s):  
Rômulo Alves Loretti ◽  
Vitor Felipe Pereira Da Costa ◽  
Daniel Geraldo De Oliveira Memoria ◽  
Andrezza Neves Barbosa ◽  
Helton Luiz Santana Oliveira ◽  
...  
Keyword(s):  
Business Intelligence ◽  
Data Science ◽  
Oil Fields ◽  
Accident Analysis ◽  
Offshore Oil

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

„Mehrwert generieren durch IoT-Analytics“

BWK ENERGIE. ◽  
2020 ◽  
Vol 72 (03) ◽  
pp. 30-32
Author(s):  
Matthias Hinkelmann ◽  
Sascha Schlosser
Keyword(s):  
Business Intelligence ◽  
Data Science ◽  
Chief Operating Officer

Die Minol-Zenner-Gruppe hat sich als Spezialist für IoT-basierte Geschäftsprozesse zum All-in-One-Anbieter gemausert. Mit der Gründung der Lehmann + Pioneers Digital GmbH (LPDG), spezialisiert auf Business Intelligence, Analytics und Data Science, wurde das noch fehlende Puzzleteil im Leistungsportfolio der Gruppe ergänzt. BWK sprach mit Matthias Hinkelmann, Chief Operating Officer bei LPDG, und Sascha Schlosser, Geschäftsführer der Zenner International GmbH & Co. KG, darüber, wohin die Digitalisierungsreise geht. Am Beispiel des jüngst ins Leben gerufenen Kooperationsprojektes „Digitale Nordallianz“ lässt sich das gut veranschaulichen.

Data Science for Business Analytics and Business Intelligence

2021 ◽  
pp. 495-508
Author(s):  
Matthias Lederer ◽  
Patrick Schmid
Keyword(s):  
Business Intelligence ◽  
Data Science ◽  
Digital Data ◽  
Competitive Advantages ◽  
Business Analytics ◽  
Simulation And Optimization ◽  
Analytical Skills ◽  
Methods And Techniques ◽  
Different Levels

Data science as the interdisciplinary collection of methods and techniques to support businesses is becoming more and more popular. This article begins with definitions and shows how systematically competitive advantages can be built up on the basis of digital data. Essential sources and types of data-driven knowledge are introduced. Then a classification of approaches of data science concepts is explained. A distinction is made between Business Analytics and Business Intelligence as different levels of analytical skills. The paper goes into depth with these concepts and presents concrete techniques, algorithms, and application scenarios. Thus, the contribution introduces State of the Art approaches to analysis, control, monitoring but also to advanced approaches such as prediction, simulation, and optimization.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

scholarly journals Blowout Accident Impact Analysis Method for the Siting of Offshore Floating Nuclear Power Plant in Offshore Oil Fields

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Zhigang Lan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Impact Analysis ◽  
Oil Spills ◽  
Oil Field ◽  
Oil Fields ◽  
Offshore Oil ◽  
The Impact

Focused on the utilization of nuclear energy in offshore oil fields, the correspondence between various hazards caused by blowout accidents (including associated, secondary, and derivative hazards) and the initiating events that may lead to accidents of offshore floating nuclear power plant (OFNPP) is established. The risk source, risk characteristics, risk evolution, and risk action mode of blowout accidents in offshore oil fields are summarized and analyzed. The impacts of blowout accident in offshore oil field on OFNPP are comprehensively analyzed, including injection combustion and spilled oil combustion induced by well blowout, drifting and explosion of deflagration vapor clouds formed by well blowouts, seawater pollution caused by blowout oil spills, the toxic gas cloud caused by well blowout, and the impact of mobile fire source formed by a burning oil spill on OFNPP at sea. The preliminary analysis methods and corresponding procedures are established for the impact of blowout accidents on offshore floating nuclear power plants in offshore oil fields, and a calculation example is given in order to further illustrate the methods.

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