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Applied Sciences
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Intelligent Data Processing and Management: Technologies and Applications
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Intelligent Data Processing and Management: Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 2024

Special Issue Editors

Prof. Dr. Wenjie Zhang

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: spatial-temporal data management; graph data analysis; big data analytics; stream processing and uncertain data management
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengyi Yang

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: graph data processing; distributed data processing; database systems
Special Issues, Collections and Topics in MDPI journals
Dr. Long Yuan

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: graph data processing; graph data analysis
Dr. Peng Cheng

E-Mail Website
Guest Editor
School of Software Engineering, East China Normal University, Shanghai 200241, China
Interests: spatial data management; blockchain; crowdsourcing

Special Issue Information

Dear Colleagues,

In the era of big data and artificial intelligence (AI), the efficient processing and management of vast, complex, and continuously growing datasets have become critical challenges for both academia and industry. Rapid advancements in machine learning, data mining, cloud computing, and distributed systems are transforming the landscape of data processing and management, allowing intelligent systems to handle large-scale data more effectively. However, the massive scale and complex variety in today’s data not only present significant challenges in scalability, security, efficiency, and accuracy but also create opportunities to leverage intelligent methods for deeper insights and innovation. This Special Issue seeks to present cutting-edge research that addresses these challenges by exploring innovative approaches to data processing, management techniques, and the integration of intelligent systems to optimize the entire data lifecycle. It will showcase recent advancements and applications in intelligent data processing, offering a comprehensive review of current approaches, methodologies, and technologies that address the complexities of modern data management.

We invite the submission of original research contributions in, but not limited to, AI-powered big data processing, AI-based data mining, intelligent data stream systems, AI-enhanced data security and privacy, AI-driven database systems, large language models (LLMs) in data management, graph data processing, spatial and temporal data analysis, scalable machine learning algorithms, advanced data mining techniques, and the application of modern techniques in data management. These advancements promise to reshape the field of data science, leading to more efficient and intelligent systems for the future.

Prof. Dr. Wenjie Zhang
Dr. Zhengyi Yang
Dr. Long Yuan
Dr. Peng Cheng
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. Applied Sciences 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

  • AI-driven data processing
  • big data analytics
  • machine learning for data management
  • scalable machine learning algorithms
  • intelligent data stream systems
  • AI-enhanced data security
  • AI-powered database systems
  • large language models (LLMs) in data management
  • graph data processing
  • spatial and temporal data analysis

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

Published Papers (2 papers)

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Research

21 pages, 8104 KiB  
Article
Development of a Modular Design and Detachable Mechanism for Safety Support Products in Winter Ice Fishing
by Cuiyu Li, Zhongjie Hao, Chen Su and Licen Bai
Appl. Sci. 2025, 15(7), 3496; https://doi.org/10.3390/app15073496 - 22 Mar 2025
Viewed by 214
Abstract
To enhance the adaptability and disassemblability of winter ice fishing safety auxiliary products, a modular design approach was introduced during the design process. Axiomatic design (AD) and design structure matrix (DSM) were employed as the theoretical guidance and methodological framework. In the design [...] Read more.
To enhance the adaptability and disassemblability of winter ice fishing safety auxiliary products, a modular design approach was introduced during the design process. Axiomatic design (AD) and design structure matrix (DSM) were employed as the theoretical guidance and methodological framework. In the design process, the “Z-mapping” method was used to reanalyze the product’s requirements, functions, and structure, progressively decomposing the overall function and constructing a corresponding design matrix. This approach converted initial user requirements into detailed functional specifications and design parameters. Geometric correlation was used as the evaluation criterion, with values assigned to the design matrix, leading to the development of a correlation matrix for the design parameters of the winter ice fishing safety auxiliary product. System clustering techniques were then applied to optimize the distribution of matrix values, allowing for the identification of functional module areas. Based on these results, a modular design scheme was proposed. The findings indicate that the Kano-AD-DSM-based design strategy significantly improved the disassemblability of the winter ice fishing safety auxiliary product, which is crucial for protecting the safety of ice fishers, reducing physical exertion, and enhancing the ice fishing experience. Moreover, the multi-module design allows the product to be flexibly configured and upgraded based on varying operational needs and personalized user requirements, significantly improving its adaptability and practicality. This research not only provides new theoretical insights for the innovative design of winter ice fishing safety auxiliary products but also offers valuable references for the modular design of similar products. Full article
(This article belongs to the Special Issue Intelligent Data Processing and Management: Technologies and Applications)
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30 pages, 5092 KiB  
Article
Kolmogorov–Arnold Finance-Informed Neural Network in Option Pricing
by Charles Z. Liu, Ying Zhang, Lu Qin and Yongfei Liu
Appl. Sci. 2024, 14(24), 11618; https://doi.org/10.3390/app142411618 (registering DOI) - 12 Dec 2024
Viewed by 1124
Abstract
Finance-Informed Neural Networks (FINNs), inspired by Physical Information Neural Networks (PINNs) and computational finance, aim to enhance risk assessment and support regulatory decision-making. Despite their promising potential, existing FINNs face significant challenges related to learning efficiency and overall performance. This paper examines the [...] Read more.
Finance-Informed Neural Networks (FINNs), inspired by Physical Information Neural Networks (PINNs) and computational finance, aim to enhance risk assessment and support regulatory decision-making. Despite their promising potential, existing FINNs face significant challenges related to learning efficiency and overall performance. This paper examines the KAFIN framework, based on the Kolmogorov–Arnold representation, which aims to improve financial modeling and analytical calculations. To evaluate the performance of FINNs, this study uses generated data instead of real market data. This choice enables controlled simulations of various financial scenarios while avoiding the complexities and noise inherent in actual market data. By relying on generated data, we are able to isolate and assess the core capabilities of KAFIN under well-defined theoretical conditions, facilitating a clearer analysis of its performance. This study focuses on European option pricing as a case study, using generated input data that simulate a range of market scenarios, including typical regulatory conditions in financial markets. Testing KAFIN under these controlled conditions allows for a rigorous evaluation of its ability to handle the complexities of pricing options across different market assumptions and regulatory constraints. Empirical results demonstrate that KAFIN significantly outperforms the baseline method, improving pricing accuracy by minimizing residual errors and aligning closely with analytical solutions. The architecture of KAFIN effectively captures the inherent complexities of option pricing, integrating financial principles with advanced computational techniques. Performance indicators reveal that KAFIN achieves lower average total losses and reduces the variability in loss components, highlighting its advantage in modeling and analyzing complex financial dependencies while ensuring compliance with regulatory standards. Full article
(This article belongs to the Special Issue Intelligent Data Processing and Management: Technologies and Applications)
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