Big Data Analytics to Improve the Decision-Making Process in Public Safety: A Case Study in Northeast Brazil

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

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Big Data Analytics in Support of the Decision Making Process

Procedia Computer Science ◽

10.1016/j.procs.2016.09.251 ◽

2016 ◽

Vol 100 ◽

pp. 1071-1084 ◽

Cited By ~ 29

Author(s):

Nada Elgendy ◽

Ahmed Elragal

Keyword(s):

Decision Making ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Decision Making Process

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Analysis of Aviation Industry for Aggressive Decision Making

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2173134 ◽

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.

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

Industrial Marketing Management ◽

10.1016/j.indmarman.2019.08.003 ◽

2020 ◽

Vol 90 ◽

pp. 523-537 ◽

Cited By ~ 10

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

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

International Journal of Operations & Production Management ◽

10.1108/ijopm-12-2020-0900 ◽

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.

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Big Data Driven Decision Making Model: A case of the South African banking sector

South African Computer Journal ◽

10.18489/sacj.v33i2.928 ◽

2021 ◽

Vol 33 (2) ◽

Author(s):

Komla Pillay ◽

Alta Van der Merwe

Keyword(s):

Decision Making ◽

Big Data ◽

South African ◽

Data Analytics ◽

Banking Sector ◽

Big Data Analytics ◽

Data Driven ◽

Decision Making Process ◽

Data Driven Decision Making ◽

Decision Making Model

The quest to develop a Big Data Driven Decision Making framework to support the incorporation of big data analytics into the decision-making process resulted in the development of a decision making model. The study was conducted within the banking sector of South Africa, with participants from three leading South African banking institutions. The conducted research followed the design science research process of awareness, suggestion, development, evaluation and conclusion. This study developed a theoretical Big Data Driven Decision Making model which illustrates the decision-making process in banking using big data. The study further determined the organizational supports that need to be in place to support the big data analytics decision-making process.

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