Cloud Computing and Big Data Mining
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 767
Special Issue Editor
2. Ageno School of Business, Golden Gate University, San Francisco, CA 94105, USA
Interests: cloud computing; enterprise software; virtualization; data center; artificial intelligence; distributed self-regulating software
Special Issue Information
Dear Colleagues,
The fields of cloud computing and big data mining are undergoing rapid evolution, underscored by significant advancements in both technologies and methodologies. Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly integrated into cloud services, leading to more intelligent and efficient cloud solutions. Edge computing infrastructure, where computing resources and storage are brought closer to end users, is pushing data processing closer to the user’s device, instead of relying on a distant central location.
Tailored IT architectures that span multiple hybrid cloud servers are enabling the flexibility to choose services from various cloud vendors or providers, and they incorporate multi-cloud solutions for distributed data analytics. The adoption of trending technologies such as the Internet of Things (IoT), blockchain, Kubernetes, and Docker is expected to pave the way for emerging technologies such as quantum computing, cloud gaming, and augmented and virtual reality (VR/AR) in the coming years. As data analytics become more intimately integrated with distributed software applications, new approaches will inevitably emerge.
This Special Issue is designed to serve as a platform for researchers and practitioners to share their most recent research findings, practical experiences, and innovative approaches within these domains. We welcome submissions of original research papers, insightful case studies, and comprehensive review articles. Topics of interest encompass, but are not limited to, architectures in cloud computing, algorithms for big data mining both in the centralized and distributed infrastructures, techniques in data analytics, and applications of machine learning.
We invite high-quality papers that discuss the technologies and methodologies advancing big data analytics while utilizing distributed cloud computing resources.
Dr. Rao Mikkilineni
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. Computers 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 1800 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 mining
- data analytics
- machine learning
- artificial intelligence
- data security
- data privacy
- internet of things (IoT)
- edge computing
- distributed computing
- data warehousing
- data processing
- cloud storage
- data visualization
- hybrid cloud servers
- edge cloud servers
- integration of the internet of things
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