scholarly journals Big Data Analytics Strategy Framework: A Case of Crowd Management During the Hajj Pilgrimage, Mecca, Saudi Arabia

2021 ◽  
Vol 14 (4) ◽  
pp. 1975-1984
Author(s):  
Hanaa Ali Aldahawi
Keyword(s):  
Saudi Arabia ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Crowd Management ◽  
Data Framework ◽  
Crowd Control ◽  
Adoption And Implementation ◽  
Big Data Applications ◽  
Paper Review

The objective of the present study was an investigation of applications of big data analytics in Hajj and Umrah for pilgrims, who come to Saudi Arabia every year for tourism and observation of religious rites as per the sacred beliefs of Islam. It has now become a necessity to see more applications of big data analytics in these pilgrimages because of the growing number of people every year. Therefore, crowd control, crowd management and conflict management are essential for reduction of stress, troubles, fatalities, accidents, theft and possible deaths during Hajj and Umrah events. Developing a predictive data analytic model for Hajj and Umrah will improve the efficiency, gross domestic product (GDP), surveillance, revenue generation, opportunities and satisfaction for the pilgrimages. In this paper, review of big data tools was presented along with their use in the decision support system and how it can be used for surveillance and crowd management. A robust big data framework applicable for Hajj and Umrah events was also presented in this paper. This was meant to aid seamless adoption and implementation of big data applications across sectors and government parastatals involved in Hajj and Umrah. The presented framework was also included all the relevant use cases related to these pilgrimages.

Importance of Big Data and Hadoop in E-Servicing

2018 ◽  
pp. 39-52
Author(s):  
Karthiga Shankar ◽  
Suganya R.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Online Information ◽  
Campaign Strategies ◽  
Security Issues ◽  
Customer Group ◽  
Big Data Applications ◽  
The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

Big Data Applications in Healthcare

Big Data ◽  
2016 ◽  
pp. 1247-1259 ◽  
Author(s):  
Jayanthi Ranjan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Big Data Applications ◽  
Business Functions ◽  
Automated Algorithms ◽  
Almost All ◽  
Tools And Techniques ◽  
Research Centres

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.

Importance of Big Data and Hadoop in E-Servicing

2022 ◽  
pp. 1634-1644
Author(s):  
Karthiga Shankar ◽  
Suganya R.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Online Information ◽  
Campaign Strategies ◽  
Security Issues ◽  
Customer Group ◽  
Big Data Applications ◽  
The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

scholarly journals The importance of big data technology

2018 ◽  
Vol 7 (4.5) ◽  
pp. 485
Author(s):  
Samson Fadiya ◽  
Arif Sari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Systems ◽  
Apache Hadoop ◽  
Industry Standard ◽  
Data Framework ◽  
Efficiency And Effectiveness ◽  
Relational Database Management ◽  
Big Data Technology

The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectiveness at a business level. Apache Hadoop is an example of such a framework. This report discusses big data, it origins, opportunities and challenges that it presents, big data analytics and the application of big data using existing big data tools or frameworks. It also discusses Apache Hadoop as a big data framework and provides a basic overview of this technology from technological and business perspectives.  

scholarly journals A Big Data Analytics Approach for the Development of Advanced Cardiology Applications

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 60 ◽  
Author(s):  
Lorenzo Carnevale ◽  
Antonio Celesti ◽  
Maria Fazio ◽  
Massimo Villari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Response Times ◽  
Distributed Processing ◽  
Big Data Analytics ◽  
Reliable System ◽  
Huge Amount ◽  
Ecg Signals ◽  
Big Data Applications ◽  
Electrocardiogram Ecg

Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues. In this paper, we focus on the arrhythmia detection problem. Specifically, our objective is to address the problem of distributed processing considering big data generated by electrocardiogram (ECG) signals in order to carry out pre-processing analysis. Specifically, an algorithm for the identification of heartbeats and arrhythmias is proposed. Such an algorithm is designed in order to carry out distributed processing over the Cloud since big data could represent the bottleneck for cardiology applications. In particular, we implemented the Menard algorithm in Apache Spark in order to process big data coming form ECG signals in order to identify arrhythmias. Experiments conducted using a dataset provided by the Physionet.org European ST-T Database show an improvement in terms of response times. As highlighted by our outcomes, our solution provides a scalable and reliable system, which may address the challenges raised by big data in healthcare.

Big data analytics for proactive industrial decision support

atp magazin ◽  
2016 ◽  
Vol 58 (09) ◽  
pp. 62 ◽  
Author(s):  
Martin Atzmueller ◽  
Benjamin Klöpper ◽  
Hassan Al Mawla ◽  
Benjamin Jäschke ◽  
Martin Hollender ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Lessons Learned ◽  
Support Functions ◽  
Big Data Applications ◽  
Big Data Technologies ◽  
Process Plants ◽  
Plant Data

Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.

scholarly journals Big Data and Climate Change

2019 ◽  
Vol 3 (1) ◽  
pp. 12 ◽  
Author(s):  
Hossein Hassani ◽  
Xu Huang ◽  
Emmanuel Silva
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Climate Science ◽  
Current Status ◽  
Future Research ◽  
Climate Data ◽  
Data Intensive ◽  
Big Data Applications

Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.

scholarly journals The Effect of Knowledge Management Systems on Measuring Success Indicators for Saudi Arabia 2030

2019 ◽  
Vol 10 (4) ◽  
pp. 31
Author(s):  
Bader A. Alyoubi
Keyword(s):  
Saudi Arabia ◽  
Big Data ◽  
Middle East ◽  
Data Analytics ◽  
Social Impact ◽  
Big Data Analytics ◽  
Strategic Objectives ◽  
Success Indicators ◽  
Vision 2030 ◽  
Common Platform

Vision 2030 is designed to place the Kingdom of Saudi Arabia (KSA) as a trading and financial hub in the Middle East. Ninety-six strategic objectives are framed for Vision 2010. Whilst these objectives are very inspiring, challenges are seen in integrating them under a single unifying framework. Unless the diverse objectives are integrated, knowledge and learning of team members are brought on a common platform to measure the success indicators, achieving the vision would be difficult. Objective of the paper is to develop a KM model that will help to measure the success indicators of Vision 2030. A literature review helped to understand the barriers, processes, and methodology of KM frameworks. The findings indicate that Vision 2030 is wide in scope with 96 loosely connected strategic objectives. An overarching framework that links all these objectives and places them on a common platform is not evident. These inputs were used to design the KM Vision 2030 model that links all the objectives and helps to gather metrics from the objectives, and measure the success of the project. Some of the metrics that can be considered are linking objectives, milestone achievement, adhering to schedule and budget, economic and social impact on people and businesses, progress in positioning KSA as the leader of Middle East, and others. Some of these measures are qualitative, whilst others are quantitative, implying that a multimodal data collection and analysis method is needed. The model suggests institution of Knowledge Champions, Communities of Practice, big data analytics, knowledge assets development and sharing, and brings all the objectives on a transparent and usable platform. A pilot study in the form of a semi-structured interview and survey was administered to five experts in the field of KM and IT systems. Their findings indicate that big data analytics can play a major role in decision-making and in measuring the project success. The findings also speak of the need to connect the strategic objectives. Recommendations are made to refine the model.

scholarly journals Big data analytics: achievements, challenges, and research trends

2020 ◽  
Vol 11 (4) ◽  
pp. 1201
Author(s):  
Andre Coelho Vaz Henriques ◽  
Fernando De Souza Meirelles ◽  
Maria Alexandra Viegas Cortez da Cunha
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Current Debate ◽  
New Wave ◽  
Field Development ◽  
Big Data Applications ◽  
Prediction Techniques ◽  
Analytical Tools ◽  
Productivity Gains

Big data applications combined with analytical tools foster prediction techniques that impact societal, economic, and political changes. After almost a decade of studies, this paper proposes to identify major debates on big data analytics, presenting its evolution over the past years and identifying its research tendencies. We limited our research to the top eight journals in information systems. Our findings suggest that big data analytics is apparently reaching a plateau, which might be confirmed by publications in the following years. The paper contributes to the current debate on big data by identifying ongoing studies in the research community. In addition, it provides a critical analysis of the field development, from its perceived benefits to its unimagined consequences. Finally, we conclude that other perspectives on big data analytics might include a new wave of studies and that new paths beyond productivity gains can be explored.

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