scholarly journals The Use of Big Data in The Oil and Gas Upstream Industry

2019 ◽  
Vol 2 (1) ◽  
pp. 14-28
Author(s):  
Khalid Istiqlal Syaifullah
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Data Analytics ◽  
Oil And Gas ◽  
Open Data ◽  
Big Data Analytics ◽  
Mineral Resources ◽  
Physical Models ◽  
Exploration And Production ◽  
The Government

A study has been done to perceive the uptake and impact of Big Data in the exploration and production of oil and gas in Indonesia compared to Norway. Interviews were conducted to officials in the Ministry of Energy and Mineral Resources (MoEMR) and the state regulator, SKK Migas. In both industries, more data is being generated more than ever in exploration, production, drilling, and operations, indicating potential application of Big Data. However, approach towards data has remained classical with physical models in opposed to common Big Data approach, which is data-driven analytics. Several impacts of Big Data in both industries are highlighted, including new demand for data analysts, the need for regulations surrounding cyber-security, improvement of safety and environment (which hasn’t been considered in Indonesia), and growing need for more trust and regulations towards open data. Open data in the two industries has seen two different trajectories with Indonesia only implementing it very recently, while the NCS has seen open data drives competition since 1999. This study produced recommendations for the government of Indonesia on open data and how uptake and application of Big Data analytics in EOR could potentially increase national petroleum production to desired levels. Keywords: Big Data, open data, oil and gas in Indonesia, Norway Continental Shelf, data analytics, EOR

Using Big Data Analytics to Assist a Smart City to Prevent Cyber Security Threats

2021 ◽  
pp. 107-124
Author(s):  
Fenio Annansingh
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart City ◽  
Data Analytics ◽  
Life Quality ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Smart Services ◽  
Prevention Model

The concept of a smart city as a means to enhance the life quality of citizens has been gaining increasing importance in recent years globally. A smart city consists of city infrastructure, which includes smart services, devices, and institutions. Every second, these components of the smart city infrastructure are generating data. The vast amount of data is called big data. This chapter explores the possibilities of using big data analytics to prevent cybersecurity threats in a smart city. It also analyzed how big data tools and concepts can solve cybersecurity challenges and detect and prevent attacks. Using interviews and an extensive review of the literature have developed the data analytics and cyber prevention model. The chapter concludes by indicating that big data analytics allow a smart city to identify and solve cybersecurity challenges quickly and efficiently.

scholarly journals Big Data Analytics for Cyber Security

2019 ◽  
Vol 2019 ◽  
pp. 1-2 ◽  
Author(s):  
Pelin Angin ◽  
Bharat Bhargava ◽  
Rohit Ranchal
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals Use of Big Data to Improve Environmental Sustainability in Developing Countries

2017 ◽  
Vol 12 (11) ◽  
pp. 249 ◽  
Author(s):  
Maged Adel Abdo Mukred ◽  
Zheng Jianguo
Keyword(s):  
Big Data ◽  
Multinational Corporations ◽  
Environmental Sustainability ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Government Sector ◽  
Wide Range ◽  
Private And Public ◽  
The Government ◽  
Set Up

Big data inhibits the ability to significantly impact a wide range of fields in an economy, from the government sector to commercial sectors like retail and healthcare. Not only has it altered the way companies assess their product’s demand and supply patterns but has also phenomenally helped in making the environment healthier in recent years. It carries the ability to identify valuable data from a huge dataset with exceptional parallel processing. This study presents the general introduction of big data bringing forth its various features and advantages along with the challenges which organizations face while using with respect to environmental sustainability. Observations have also been made on the findings of various researches, and studies and surveys performed by some international organizations in the recent years on the urgent need of taking necessary measures and initiatives to prevent further depletion of natural resources thus making the environment sustainable. Making the issue the study aim, future studies must intend to explore how multinational corporations can enhance environmental sustainability through big data analytics. Lastly, recommendations have been made to organisations– private and public in hiring adequate expertise and set-up, thereby making big data analytics more efficient and reliable.

The Strategic Value of Big Data Analytics in Health Care Policy-Making

2018 ◽  
Vol 14 (3) ◽  
pp. 20-33 ◽  
Author(s):  
Hamed M. Zolbanin ◽  
Dursun Delen ◽  
Sushil K Sharma
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Health Care Policy ◽  
Big Data Analytics ◽  
Advantages And Disadvantages ◽  
Strategic Value ◽  
Before And After ◽  
The Government ◽  
New Policies

This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymakers to obtain a better understanding of loopholes and to propose more effective policies in prospective endeavors.

scholarly journals The dynamics of traffic congestion: a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning

2019 ◽  
Vol 15 (2) ◽  
pp. 1086
Author(s):  
M. Ali ◽  
T. K. Sheng ◽  
K. M. Yusof ◽  
M. R. Suhaili ◽  
N. E. Ghazali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Traffic Congestion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Active Role ◽  
Machine Learning Algorithms ◽  
Asia Pacific ◽  
The Government

Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue.

Big Data Analytics in Cyber Security

2018 ◽  
Vol 6 (7) ◽  
pp. 731-734
Author(s):  
Ashish Bajpai ◽  
Dayanand . ◽  
Arushi Arya
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Data Analytics ◽  
Big Data Analytics

Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management

2019 ◽  
pp. 70-99
Author(s):  
Murat Ozemre ◽  
Ozgur Kabadurmus
Keyword(s):  
Big Data ◽  
International Trade ◽  
Supply Chain ◽  
Data Analytics ◽  
Open Data ◽  
Big Data Analytics ◽  
Global Supply Chain ◽  
Factors Affecting ◽  
Export Volume

As the supply chains become more global, the operations (such as procurement, production, warehousing, sales, and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However, in today's competitive and complex global supply chain environments, making accurate forecasts has become significantly difficult. In this chapter, the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology, the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts.

Sign in / Sign up

Export Citation Format

Share Document