scholarly journals Analysis of Aviation Industry for Aggressive Decision Making

2021 ◽  
pp. 625-631
Author(s):  
Mohmmed Ali Asgar Niazi ◽  
Dr. Sheikh Fahad Ahmad
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Informed Consent ◽  
General Public ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Aviation Industry ◽  
Business Objectives ◽  
Public Areas

Big Data Analytics is very useful for the business users and data scientists. It is very useful to take better, faster and right decision for the organization. Organizations and individuals should exhibit the circumspection while utilizing Big Data. In this work we intend to develop a methodology for getting ethical access of big data and ethically scrutinize it to attain the business objectives. We consider the case study of aviation sector, formulate some questions to upraise the system.  We attain the ethical permission from twitter for this purpose. We consider the tweets of general public as they were posted in public areas and falls under informed consent category.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making

2020 ◽  
Vol 90 ◽  
pp. 523-537 ◽  
Author(s):  
Erick Kauffmann ◽  
Jesús Peral ◽  
David Gil ◽  
Antonio Ferrández ◽  
Ricardo Sellers ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Big Data ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Marketing Decision

Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carine Dominguez-Péry ◽  
Rana Tassabehji ◽  
Lakshmi Narasimha Raju Vuddaraju ◽  
Vikhram Kofi Duffour
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Emergency Response ◽  
Early Warning System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Warning System ◽  
Content Type

PurposeThis paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents.Design/methodology/approachBased on secondary data, the authors developed a narrative case study of the MV Wakashio maritime disaster. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors applied BDA techniques to a dataset of real-time tweets to evaluate the unfolding operational response to the maritime emergency.FindingsThe authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. We highlighted the specific roles of three main stakeholders (the ship's organisation, official institutions and the wider community). Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents.Research limitations/implicationsThis study is limited to Twitter data and one case. Our conceptual model needs to be operationalised.Practical implicationsComACom will improve decision-making to minimise human errors in maritime accidents.Social implicationsEmergency response will be improved by including the voices of the wider community.Originality/valueComACom conceptualises an early warning system using emerging BDA/AI technologies to improve safety in maritime transportation.

Assessment of rice yield prediction models based on big data analytics for better supply chain decision-making in Thailand

2021 ◽  
Vol 12 (3) ◽  
pp. 221
Author(s):  
John K.M. Kuwornu ◽  
Chutiporn Anutariya ◽  
Attaphongse Taparugssanagorn ◽  
Sumanya Ngandee
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Prediction Models ◽  
Rice Yield ◽  
Big Data Analytics ◽  
Yield Prediction
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