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Electronics
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Big Data and Cloud Computing: Innovations and Challenges
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Big Data and Cloud Computing: Innovations and Challenges

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 8662

Special Issue Editors

Dr. Ting Wang

E-Mail Website
Guest Editor
Software Engineering Institute, East China Normal University, Shanghai 200062, China
Interests: cloud computing; machine learning; intelligent networking
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Wang

E-Mail
Co-Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: cloud computing; Internet of Things; wireless big data; artificial intelligence
Dr. Subrota Kumar Mondal

E-Mail Website
Co-Guest Editor
Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
Interests: cloud computing; networks and distributed systems; blockchain; deep learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue (SI) encourages authors to present the latest research achievements in new theories and practical solutions related to big data and cloud computing. Cloud computing, as a promising computing paradigm, redefines the service mode of the whole IT industry, and changes the use mode of software and hardware resources which can be accessible at any time, used on demand, expanded as needed, and operated on a pay-as-you-go basis. Meanwhile, because of the enormous volume and extremely high processing complexity of big data, cloud computing has become an ideal platform for big data. In turn, cloud application development is also fueled by big data. Thus, there are infinite possibilities when we combine big data and cloud computing. However, there are still many challenges and open issues in data transmission, security, privacy, scalability, agility, cost, accessibility, resilience, energy efficiency, computation efficiency, intelligence, etc. The main aim of this Special Issue is to seek high-quality submissions that highlight emerging applications with advanced technologies and address recent breakthroughs in the design of big data and cloud systems. The topics of interest include, but are not limited to:

  • Resource allocation;
  • Task scheduling;
  • Workload load balance;
  • Distributed computing architectures;
  • Cloud data center networks;
  • Big data analytics;
  • Big data security and privacy;
  • Data recovery;
  • Data integrity;
  • Artificial intelligence for big data and clouds;
  • Energy efficiency in big data and clouds;
  • Performance evaluation of big data and cloud systems;
  • Pricing and accessibility of cloud resources;
  • Edge cloud for big data.

Dr. Ting Wang
Dr. Lu Wang
Dr. Subrota Kumar Mondal
Guest Editors

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • resource allocation
  • task scheduling
  • workload load balance
  • distributed computing architectures
  • cloud data center networks
  • big data analytics
  • big data security and privacy
  • data recovery
  • data integrity
  • artificial intelligence for big data and clouds
  • energy efficiency in big data and clouds
  • performance evaluation of big data and cloud systems
  • pricing and accessibility of cloud resources
  • edge cloud for big data

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

34 pages, 1856 KiB  
Article
Security Quantification of Container-Technology-Driven E-Government Systems
by Subrota Kumar Mondal, Tian Tan, Sadia Khanam, Keshav Kumar, Hussain Mohammed Dipu Kabir and Kan Ni
Electronics 2023, 12(5), 1238; https://doi.org/10.3390/electronics12051238 - 4 Mar 2023
Cited by 5 | Viewed by 2252
Abstract
With the rapidly increasing demands of e-government systems in smart cities, a myriad of challenges and issues are required to be addressed. Among them, security is one of the prime concerns. To this end, we analyze different e-government systems and find that an [...] Read more.
With the rapidly increasing demands of e-government systems in smart cities, a myriad of challenges and issues are required to be addressed. Among them, security is one of the prime concerns. To this end, we analyze different e-government systems and find that an e-government system built with container-based technology is endowed with many features. In addition, overhauling the architecture of container-technology-driven e-government systems, we observe that securing an e-government system demands quantifying security issues (vulnerabilities, threats, attacks, and risks) and the related countermeasures. Notably, we find that the Attack Tree and Attack–Defense Tree methods are state-of-the-art approaches in these aspects. Consequently, in this paper, we work on quantifying the security attributes, measures, and metrics of an e-government system using Attack Trees and Attack–Defense Trees—in this context, we build a working prototype of an e-government system aligned with the United Kingdom (UK) government portal, which is in line with our research scope. In particular, we propose a novel measure to quantify the probability of attack success using a risk matrix and normal distribution. The probabilistic analysis distinguishes the attack and defense levels more intuitively in e-government systems. Moreover, it infers the importance of enhancing security in e-government systems. In particular, the analysis shows that an e-government system is fairly unsafe with a 99% probability of being subject to attacks, and even with a defense mechanism, the probability of attack lies around 97%, which directs us to pay close attention to e-government security. In sum, our implications can serve as a benchmark for evaluation for governments to determine the next steps in consolidating e-government system security. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing: Innovations and Challenges)
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20 pages, 3096 KiB  
Article
An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval
by Chengzhang Zhu, Zixi Liu, Beiji Zou, Yalong Xiao, Meng Zeng, Han Wang and Ziang Fan
Electronics 2023, 12(4), 987; https://doi.org/10.3390/electronics12040987 - 16 Feb 2023
Cited by 1 | Viewed by 2137
Abstract
In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified [...] Read more.
In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified access to medical data. In order to improve the retrieval speed, the recognition model S-TCR and the dynamic management algorithm SL-TCR, based on the behavior characteristics of access, were proposed to identify the frequently accessed hot data and dynamically manage the data storage medium as to maximize the system access performance. In order to improve the search performance of keys, an optimized secondary index strategy was proposed to reduce I/O overhead and optimize the search performance of non-primary key indexes. Comparative experiments were conducted on real medical data sets. The experimental results show that the optimized retrieval model can meet the needs of hot data access and diversified medical data retrieval. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing: Innovations and Challenges)
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22 pages, 2785 KiB  
Article
Differentially Private Timestamps Publishing in Trajectory
by Liang Yan, Hao Wang, Zhaokun Wang, Tingting Wu, Wandi Fu and Xu Zhang
Electronics 2023, 12(2), 361; https://doi.org/10.3390/electronics12020361 - 10 Jan 2023
Viewed by 1482
Abstract
In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have [...] Read more.
In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user’s specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user’s geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing: Innovations and Challenges)
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18 pages, 867 KiB  
Article
Framework for Structuring Big Data Projects
by Gustavo Grander, Luciano Ferreira Da Silva, Ernesto Del Rosário Santibañez Gonzalez and Renato Penha
Electronics 2022, 11(21), 3540; https://doi.org/10.3390/electronics11213540 - 30 Oct 2022
Cited by 3 | Viewed by 1668
Abstract
This article aims to present a framework for structuring Big Data projects. The methodological procedures were divided into two phases: in-depth interview and focus group. The first phase embraces 12 in-depth individual interviews. In the second phase, three sessions of interviews with focus [...] Read more.
This article aims to present a framework for structuring Big Data projects. The methodological procedures were divided into two phases: in-depth interview and focus group. The first phase embraces 12 in-depth individual interviews. In the second phase, three sessions of interviews with focus groups were applied. Both phases had as research subjects professionals with experience in Big Data projects. The analysis process was based on categorization through theory-driven and data-driven codes. Based on our analysis, it was possible to present a definition of a Big Data project and explore the beginning and its phases. We also identified 17 different critical factors in Big Data projects and proposed a discussion on technical and behavioral skills and decision-making issues in Big Data projects. We also developed and validated a framework to help structure a Big Data project. As the main theoretical contribution, our study aligns with a growing body of researchers who are proposing to debate Big Data projects, and with that increasing maturity on the topic. As a practical contribution, we present a framework that we hope will contribute to professionals working in the area, helping to conduct a Big Data project through a systemic view. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing: Innovations and Challenges)
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