Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.1
Journals
Information
Special Issues
Big Data, IoT and Cloud Computing
share announcement

Big Data, IoT and Cloud Computing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (14 June 2023) | Viewed by 21064

Special Issue Editor

Dr. Habtamu Abie

E-Mail Website
Guest Editor
Norwegian Computing Center/Norsk Regnesentral, 0373 Oslo, Norway
Interests: adaptive and evolving security (AES); cybersecurity; AI for security; cognitive IoT security (CogSec); game theory; evolving algorithms; security for distributed systems; distributed object computing (CORBA, EJB, DCOM/ActiveX); digital rights management; privacy; trust; policy and risk management

Special Issue Information

Dear Colleagues,

This Special Issue will collect extended versions of selected papers presented at the 2021 International Conference on Big Data, the IoT, and Cloud Computing (ICBICC 2021) and other contributions.

ICBICC 2021 and this Special Issue welcome researchers, engineers, scientists, and industry professionals to an open forum where advances in the field of big data, the IoT, and cloud computing can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field. Leading researchers and industry experts from around the globe will be presenting the latest studies through papers and oral presentations. Authors of invited papers should be aware that the final submitted manuscript to the Special Issue must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Dr. Habtamu Abie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data algorithms, models, applications, and services
  • Big data search, mining, and visualization
  • Big data analytics and metrics
  • IoT protocols and standards
  • IoT and 5G networks
  • IoT applications, services, and implementations
  • Cloud services and applications
  • Cloud programming models and tools
  • Cloud storage and databases
  • Converged ICT technologies
  • Converged ICT applications
  • Converged ICT services

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 2100 KiB  
Article
Extended Reality in Marketing—A Multiple Case Study on Internet of Things Platforms
by Ralf Wagner and Diana Cozmiuc
Information 2022, 13(6), 278; https://0-doi-org.brum.beds.ac.uk/10.3390/info13060278 - 27 May 2022
Cited by 11 | Viewed by 5928
Abstract
This research highlights how cloud platform as a service technologies host extended reality technologies and convergent technologies in integrated solutions. It was only around 2019 that scholarly literature conceptualized the role of extended reality, that is, augmented reality, virtual reality, and mixed reality, [...] Read more.
This research highlights how cloud platform as a service technologies host extended reality technologies and convergent technologies in integrated solutions. It was only around 2019 that scholarly literature conceptualized the role of extended reality, that is, augmented reality, virtual reality, and mixed reality, in the marketing function. This article is a multiple case study on the leading eleven platform as a service vendors. They provide the programming technology required to host software as a service in the cloud, making the software available from everywhere. Of the eleven cases, 10% integrate technologies in solutions. Research results show that extended reality technologies reinvent digital marketing; as part of this, they shape the customer delivery model in terms of customer value proposition; favor the choice of customer channel (the omnichannel); possibly lead to new customer relationships, such as cocreation; and reach global mass customers. Extended reality in the delivery model is complemented by other technologies in the operating model. These combinations provide the foundations of the business models, which are either network or platform business models. This study identifies a number of solutions enabled by extended reality, which have an integrated goal in the form of customer value contribution and are to be studied in further articles. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
►▼ Show Figures

Figure 1

14 pages, 470 KiB  
Article
Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece
by Ilias Moumtzidis, Maria Kamariotou and Fotis Kitsios
Information 2022, 13(4), 196; https://0-doi-org.brum.beds.ac.uk/10.3390/info13040196 - 14 Apr 2022
Cited by 11 | Viewed by 3883
Abstract
Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the [...] Read more.
Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
►▼ Show Figures

Figure 1

16 pages, 774 KiB  
Article
Performance Evaluation of Distributed Database Strategies Using Docker as a Service for Industrial IoT Data: Application to Industry 4.0
by Theodosios Gkamas, Vasileios Karaiskos and Sotirios Kontogiannis
Information 2022, 13(4), 190; https://0-doi-org.brum.beds.ac.uk/10.3390/info13040190 - 09 Apr 2022
Cited by 10 | Viewed by 3999
Abstract
Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored [...] Read more.
Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored in the cloud. This paper presents the authors’ proposition for cloudcentric sensory measurements and measurements acquisition. Then, it focuses on evaluating industrial cloud storage engines for sensory functions, experimenting with three open-source types of distributed Database Management Systems (DBMS); MongoDB and PostgreSQL, with two forms of PostgreSQL schemes (Javascript Object Notation (JSON)-based and relational), against their respective horizontal scaling strategies. Several experimental cases have been performed to measure database queries’ response time, achieved throughput, and corresponding failures. Three distinct scenarios have been thoroughly tested, the most common but widely used: (i) data insertions, (ii) select/find queries, and (iii) queries related to aggregate correlation functions. The experimental results concluded that PostgreSQL with JSON achieves a 5–57% better response than MongoDB for the insert queries (cases of native, two, and four shards implementations), while, on the contrary, MongoDB achieved 56–91% higher throughput than PostgreSQL for the same set up. Furthermore, for the data insertion experimental cases of six and eight shards, MongoDB performed 13–20% more than Postgres in response time, achieving × 2 times higher throughput. Relational PostgreSQL was × 2 times faster than MongoDB in its standalone implementation for selection queries. At the same time, MongoDB achieved 19–31% faster responses and 44–63% higher throughput than PostgreSQL in the four tested sharding subcases (two, four, six, eight shards), accordingly. Finally, the relational PostgreSQL outperformed MongoDB and PostgreSQL JSON significantly in all correlation function experiments, with performance improvements from MongoDB, closing the gap with PostgreSQL towards minimizing response time to 26% and 3% for six and eight shards, respectively, and achieving significant gains towards average achieved throughput. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
►▼ Show Figures

Figure 1

18 pages, 2930 KiB  
Article
Apache Spark and MLlib-Based Intrusion Detection System or How the Big Data Technologies Can Secure the Data
by Otmane Azeroual and Anastasija Nikiforova
Information 2022, 13(2), 58; https://0-doi-org.brum.beds.ac.uk/10.3390/info13020058 - 24 Jan 2022
Cited by 24 | Viewed by 5704
Abstract
Since the turn of the millennium, the volume of data has increased significantly in both industries and scientific institutions. The processing of these volumes and variety of data we are dealing with are unlikely to be accomplished with conventional software solutions. Thus, new [...] Read more.
Since the turn of the millennium, the volume of data has increased significantly in both industries and scientific institutions. The processing of these volumes and variety of data we are dealing with are unlikely to be accomplished with conventional software solutions. Thus, new technologies belonging to the big data processing area, able to distribute and process data in a scalable way, are integrated into classical Business Intelligence (BI) systems or replace them. Furthermore, we can benefit from big data technologies to gain knowledge about security, which can be obtained from massive databases. The paper presents a security-relevant data analysis based on the big data analytics engine Apache Spark. A prototype intrusion detection system is developed aimed at detecting data anomalies through machine learning by using the k-means algorithm for clustering analysis implemented in Sparks MLlib. The extraction of features to detect anomalies is currently challenging because the problem of detecting anomalies is not actively and exhaustively monitored. The detection of abnormal data can be effectuated by using relevant data that are already in companies’ and scientific organizations’ possession. Their interpretation and further processing in a continuous manner can sufficiently contribute to anomaly and intrusion detection. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop