Towards adopting Big Data technologies by mobile networks operators: A Moroccan case study

2016 ◽  
Author(s):  
Houda Daki ◽  
Asmaa El Hannani ◽  
Abdelhak Aqqal ◽  
Abdelfattah Haidine ◽  
Aziz Dahbi ◽  
...  
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Big Data Technologies

A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

2019 ◽  
Vol 9 (2) ◽  
pp. 16-30 ◽  
Author(s):  
Hind Bangui ◽  
Mouzhi Ge ◽  
Barbora Buhnova
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Future Internet ◽  
Research Challenges ◽  
Initial Stage ◽  
Big Data Technologies

Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

Big Data Application in Entrepreneurship

2020 ◽  
pp. 214-240
Author(s):  
León Darío Parra ◽  
Milenka Linneth Argote Cusi
Keyword(s):  
Big Data ◽  
Modern Society ◽  
Advantages And Disadvantages ◽  
The Core ◽  
Data Application ◽  
Big Data Technologies ◽  
Big Data Application ◽  
Global Entrepreneurship ◽  
Gem Data

Modern society generates about 7 Zetabytes each year, of which 75% comes from the connectivity of individuals to social networks. In this regard, the chapter presents a case study of the application of big data technologies for entrepreneurial analysis using global entrepreneurship monitor (GEM) data as a new tool of analysis. Therefore, the core of this chapter is to present the methodology that was used to develop and implement the big data app of GEM as well as the main results of project. On the other hand, the chapter remarks the advantages and disadvantages of this kind of technology for the case of GEM data. Finally, it presents the respective dashboards that interrelate the gem data with Word Bank indicators as a case study of the application of big data for entrepreneurship research.

scholarly journals ‘Ai Theory of Justice’: Using Rawlsian Approaches to Legislate Better on Machine Learning in Government

Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
John Rawls ◽  
Data Analytics ◽  
The Public ◽  
Theory Of Justice ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.

A CASE STUDY: INTEGRATING BIG DATA TECHNOLOGIES FOR IOT SECURITY INTO EXPERIMENTAL LAB SESSION

2021 ◽  
Author(s):  
Lyubomir Gotsev ◽  
Boyan Jekov ◽  
Eugenia Kovatcheva ◽  
Roumen Nikolov ◽  
Ilian Barzev ◽  
...  
Keyword(s):  
Big Data ◽  
Iot Security ◽  
Big Data Technologies

Withdrawal Notice: Big Data Opportunities in the Telecommunication Sector: A Case Study of a Moroccan Mobile Networks Operator

2018 ◽  
Vol 13 ◽  
Author(s):  
Houda Daki ◽  
Asmaa Elhannani ◽  
Abdelhak Aqqal ◽  
Abdelfattah Haidine ◽  
Aziz Dahbi
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Mobile Networks ◽  
Editorial Policy ◽  
Telecommunication Sector ◽  
Editorial Policies ◽  
Recent Advances ◽  
Copyright Permission ◽  
Legal Right

The article has been withdrawn at the request of the authors and editor of the journal Recent Advances in Computer Science and Communications. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Big Data Technologies: A Case Study

2017 ◽  
Vol 9 (4) ◽  
pp. 639
Author(s):  
Padmavathi Vanka ◽  
T. Sudha
Keyword(s):  
Big Data ◽  
Big Data Technologies

scholarly journals Efficient processing of complex XSD using Hive and Spark

2021 ◽  
Vol 7 ◽  
pp. e652
Author(s):  
Diana Martinez-Mosquera ◽  
Rosa Navarrete ◽  
Sergio Luján-Mora
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Mobile Networks ◽  
Real Life ◽  
Real Data ◽  
Xml Schema ◽  
Apache Spark ◽  
Data Sets ◽  
Apache Hive

The eXtensible Markup Language (XML) files are widely used by the industry due to their flexibility in representing numerous kinds of data. Multiple applications such as financial records, social networks, and mobile networks use complex XML schemas with nested types, contents, and/or extension bases on existing complex elements or large real-world files. A great number of these files are generated each day and this has influenced the development of Big Data tools for their parsing and reporting, such as Apache Hive and Apache Spark. For these reasons, multiple studies have proposed new techniques and evaluated the processing of XML files with Big Data systems. However, a more usual approach in such works involves the simplest XML schemas, even though, real data sets are composed of complex schemas. Therefore, to shed light on complex XML schema processing for real-life applications with Big Data tools, we present an approach that combines three techniques. This comprises three main methods for parsing XML files: cataloging, deserialization, and positional explode. For cataloging, the elements of the XML schema are mapped into root, arrays, structures, values, and attributes. Based on these elements, the deserialization and positional explode are straightforwardly implemented. To demonstrate the validity of our proposal, we develop a case study by implementing a test environment to illustrate the methods using real data sets provided from performance management of two mobile network vendors. Our main results state the validity of the proposed method for different versions of Apache Hive and Apache Spark, obtain the query execution times for Apache Hive internal and external tables and Apache Spark data frames, and compare the query performance in Apache Hive with that of Apache Spark. Another contribution made is a case study in which a novel solution is proposed for data analysis in the performance management systems of mobile networks.

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