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Electronics
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New Trends in Cloud Computing for Big Data Analytics
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New Trends in Cloud Computing for Big Data Analytics

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 279

Special Issue Editor

Dr. Ming-Shen Jian

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Formosa University, Huwei Township 632, Taiwan
Interests: open-source cloud computing; artificial intelligence; big data; AIoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue aims to explore the latest advancements, innovative solutions, and emerging challenges in the intersection of cloud computing and big data analytics. 

The scope of this Special Issue encompasses a wide range of aspects related to how cloud computing technologies can be effectively utilized to handle, process, and analyze vast amounts of big data. It delves into the potential of cloud platforms in providing scalable infrastructure, cost effective solutions, and high-performance computing capabilities for big data applications. 

This Special Issue will focus on (but is not limited to) the following topics: First, it will cover cloud-based big data processing frameworks, such as how new architectures are designed to optimize data flow and computational efficiency. Second, it will cover security and privacy issues in cloud-enabled big data analytics are crucial, including techniques to protect sensitive data during storage and processing. Third, the integration of artificial intelligence and machine learning algorithms with cloud-based big data systems will be covered, exploring how these combinations can enhance data analysis accuracy and prediction capabilities. Fourth, this Special Issue will cover energy-efficient cloud computing strategies for big data analytics, aiming to reduce the environmental impact while maintaining performance.

Dr. Ming-Shen Jian
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. 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

  • cloud computing
  • big data analytics
  • innovation
  • challenges
  • security
  • AI integration

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Published Papers (1 paper)

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Research

22 pages, 979 KB  
Article
Multi-Modal Semantic Fusion for Smart Contract Vulnerability Detection in Cloud-Based Blockchain Analytics Platforms
by Xingyu Zeng, Qiaoyan Wen and Sujuan Qin
Electronics 2025, 14(21), 4188; https://doi.org/10.3390/electronics14214188 (registering DOI) - 27 Oct 2025
Abstract
With the growth of trusted computing demand for big data analysis, cloud computing platforms are reshaping trusted data infrastructure by integrating Blockchain as a Service (BaaS), which uses elastic resource scheduling and heterogeneous hardware acceleration to support petabyte level multi-institution data security exchange [...] Read more.
With the growth of trusted computing demand for big data analysis, cloud computing platforms are reshaping trusted data infrastructure by integrating Blockchain as a Service (BaaS), which uses elastic resource scheduling and heterogeneous hardware acceleration to support petabyte level multi-institution data security exchange in medical, financial, and other fields. As the core hub of data-intensive scenarios, the BaaS platform has the dual capabilities of privacy computing and process automation. However, its deep dependence on smart contracts generates new code layer vulnerabilities, resulting in malicious contamination of analysis results. The existing detection schemes are limited to the perspective of single-source data, which makes it difficult to capture both global semantic associations and local structural details in a cloud computing environment, leading to a performance bottleneck in terms of scalability and detection accuracy. To address these challenges, this paper proposes a smart contract vulnerability detection method based on multi-modal semantic fusion for the blockchain analysis platform of cloud computing. Firstly, the contract source code is parsed into an abstract syntax tree, and the key code is accurately located based on the predefined vulnerability feature set. Then, the text features and graph structure features of key codes are extracted in parallel to realize the deep fusion of them. Finally, with the help of attention enhancement, the vulnerability probability is output through the fully connected network. The experiments on Ethereum benchmark datasets show that the detection accuracy of our method for re-entrancy vulnerability, timestamp vulnerability, overflow/underflow vulnerability, and delegatecall vulnerability can reach 92.2%, 96.3%, 91.4%, and 89.5%, surpassing previous methods. Additionally, our method has the potential for practical deployment in cloud-based blockchain service environments. Full article
(This article belongs to the Special Issue New Trends in Cloud Computing for Big Data Analytics)
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