Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
Future Technologies for Data Management, Processing and Application
share announcement

Future Technologies for Data Management, Processing and Application

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 7208

Special Issue Editor

Prof. Dr. Duc Tai Le

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: wireless ad hoc and sensor networks; intelligent Internet of Things; network softwarization; medical image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, we have experienced intelligent convergence of technologies, and the most important factor in the centre of it is “Data”. Various business models exploit the importance of data in their favour, and it is safe to say that data have become the biggest commodity of the technological world, and current trends only suggest an increase in its importance. Data's origin can be any technological domain, but their use in any domain is determined by its processing and analysis. One of the major challenges in harnessing the boundaryless capabilities of data is their effective management to enable generalized and targeted processing for certain types of applications. This involves converging diverse topics like information management, intelligent information processing, interaction management, and networking.

We will invite the best paper awardees and top 10 articles from IMCOM 2024 and 2025 to submit their conference papers in this Special Issue. In addition to invited papers, the Special Issue will solicit an open call for investigators to contribute original research articles as well as review articles that will stimulate continuing efforts from researchers in both the academia and industry sectors to present novel approaches, algorithms and applications on data management and processing for innovative services and new paradigm. Topics of interest include, but are not limited to:

  • Data management for the Internet of Things and sensor systems;
  • Mobile cloud computing and data management;
  • Context-aware computing for intelligent mobile services;
  • Data stream processing in mobile/sensor networks;
  • Personalized routing, eco-routing, and routing for mobile networks;
  • Data-intensive mobile computing;
  • Streams, sensor networks, complex event processing;
  • Innovative applications driven by mobile data;
  • Mobile data analytics;
  • Applications and challenges of in-network computation;
  • Data management for connected cars, intelligent transportation systems, and smart spaces;
  • Privacy, trust and security in databases;
  • Data fusion and integration;
  • Knowledge discovery, clustering, and data mining;
  • Machine learning for data management and vice versa.

Prof. Dr. Duc Tai Le
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

  • data mining
  • mobile networks
  • sensor networks
  • data management
  • data security
  • data fusion and integration

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (9 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

32 pages, 863 KiB  
Article
Preserving Clusters in Synthetic Data Sets Based on Correlations and Distributions
by Lucija Petricioli, Luka Humski and Mihaela Vranić
Electronics 2025, 14(11), 2230; https://doi.org/10.3390/electronics14112230 - 30 May 2025
Viewed by 268
Abstract
The rising popularity of machine learning has resulted in quality data becoming increasingly valuable. However, in some cases, the data are too sparse to effectively train an algorithm or the data cannot be disclosed to unaffiliated researchers due to privacy concerns. The sparsity [...] Read more.
The rising popularity of machine learning has resulted in quality data becoming increasingly valuable. However, in some cases, the data are too sparse to effectively train an algorithm or the data cannot be disclosed to unaffiliated researchers due to privacy concerns. The sparsity of data may also affect various data analyses that require a certain volume of data to be accurate. One possible solution to the aforementioned problems is data generation. However, to be a viable solution, data generation must simulate real-life data well. To this end, this paper tests whether a previously presented iterative data generation method that generates synthetic data sets based on the attribute distributions and correlations of a real-life data set can faithfully reproduce a clustered data set. The approach is shown to be ineffective for the proposed application, and consequently, a new method is introduced that might preserve the clusters present in the real-life data set. The new method is demonstrated to not only preserve the clusters within the synthetic data set, but also improve the similarity of the attribute correlations of the synthetic data set and the real-life data set. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

17 pages, 1262 KiB  
Article
Time Series Forecasting via an Elastic Optimal Adaptive GM(1,1) Model
by Teng Li, Jiajia Nie, Guozhi Qiu, Zhen Li, Cun Ji and Xueqing Li
Electronics 2025, 14(10), 2071; https://doi.org/10.3390/electronics14102071 - 20 May 2025
Viewed by 176
Abstract
The GM(1,1) model is a well-established approach for time series forecasting, demonstrating superior effectiveness with limited data and incomplete information. However, its performance often degrades in dynamic systems, leading to obvious prediction errors. To address this impediment, we propose an elastic optimal adaptive [...] Read more.
The GM(1,1) model is a well-established approach for time series forecasting, demonstrating superior effectiveness with limited data and incomplete information. However, its performance often degrades in dynamic systems, leading to obvious prediction errors. To address this impediment, we propose an elastic optimal adaptive GM(1,1) model, dubbed EOAGM, to improve forecasting performance. Specifically, our proposed EOAGM dynamically optimizes the sequence length by discarding outdated data and incorporating new data, reducing the influence of irrelevant historical information. Moreover, we introduce a stationarity test mechanism to identify and adjust sequence data fluctuations, ensuring stability and robustness against volatility. Additionally, the model refines parameter optimization by incorporating predicted values into candidate sequences and assessing their impact on subsequent forecasts, particularly under conditions of data fluctuation or anomalies. Experimental evaluations across multiple real-world datasets demonstrate the superior prediction accuracy and reliability of our model compared to six baseline approaches. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

13 pages, 978 KiB  
Article
Automatic Image Distillation with Wavelet Transform and Modified Principal Component Analysis
by Nikolay Metodiev Sirakov and Long H. Ngo
Electronics 2025, 14(7), 1357; https://doi.org/10.3390/electronics14071357 - 28 Mar 2025
Viewed by 566
Abstract
Data distillation is an emerging research area, attracting the attention of machine learning (ML) and big data scientists and experts. The main goal of a distillation approach is to generate a compact dataset that preserves the essential characteristics of a larger one. In [...] Read more.
Data distillation is an emerging research area, attracting the attention of machine learning (ML) and big data scientists and experts. The main goal of a distillation approach is to generate a compact dataset that preserves the essential characteristics of a larger one. In our study, we considered an initial large set of images and developed a novel method for distilling images from the initial set. The method combined discrete wavelet transform (DWT) and modified principal component analysis (M-PCA). Hence, our method first transforms images into vectors of low-band (LL) wavelet coefficients and then applies M-PCA to modify and reduce the number of vectors rather than their dimensionality. This distinguishes our approach from the traditional PCA method. We implemented the new method in Python 3.10 and validated it on public image databases, including Extended YaleB, digit-MNIST, and the ISIC2020. We demonstrated that creating a dictionary from a small set of distilled images and training a sparse representation wavelet-based classifier (SRWC) provides higher accuracy if compared to a classification when the SRWC method is trained with the entire initial training set of images. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Graphical abstract

19 pages, 946 KiB  
Article
Efficient Ensemble of Deep Neural Networks for Multimodal Punctuation Restoration and the Spontaneous Informal Speech Dataset
by Homayoon Beigi and Xing Yi Liu
Electronics 2025, 14(5), 973; https://doi.org/10.3390/electronics14050973 - 28 Feb 2025
Viewed by 858
Abstract
Punctuation restoration plays an essential role in the postprocessing procedure of automatic speech recognition, but model efficiency is a key requirement for this task. To that end, we present EfficientPunct, an ensemble method with a multimodal time-delay neural network that outperforms the [...] Read more.
Punctuation restoration plays an essential role in the postprocessing procedure of automatic speech recognition, but model efficiency is a key requirement for this task. To that end, we present EfficientPunct, an ensemble method with a multimodal time-delay neural network that outperforms the current best model by 1.0 F1 point while using less than a tenth of its network parameters for inference. This work further streamlines a speech recognizer and a BERT implementation to efficiently output hidden layer acoustic embeddings and text embeddings in the context of punctuation restoration. Here, forced alignment and temporal convolutions are used to eliminate the need for attention-based fusion, greatly increasing computational efficiency and improving performance. EfficientPunct sets a new state of the art with an ensemble that weighs BERT’s purely language-based predictions slightly more than the multimodal network’s predictions. Although EfficientPunct shows great promise, from a different perspective, to date, another important challenge in the field has been the fact that punctuation restoration models have been evaluated almost solely on well-structured, scripted corpora. However, real-world ASR systems and postprocessing pipelines typically apply to spontaneous speech with significant irregularities, stutters, and deviations from perfect grammar. To address this important discrepancy, we also introduce SponSpeech, a punctuation restoration dataset derived from informal speech sources, which includes punctuation and casing information. In addition to publicly releasing the dataset, the authors have contributed by providing a filtering pipeline that can be used to generate more data. This filtering pipeline examines the quality of both the speech audio and the transcription text. A challenging test set is also carefully constructed, aimed at evaluating the models’ ability to leverage audio information to predict, otherwise grammatically ambiguous, punctuation. SponSpeech has been made available to the public, along with all code for dataset building and model runs. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

13 pages, 736 KiB  
Article
Implicit Identity Authentication Method Based on User Posture Perception
by Bo Hu, Shigang Tang, Fangzheng Huang, Guangqiang Yin and Jingye Cai
Electronics 2025, 14(5), 835; https://doi.org/10.3390/electronics14050835 - 20 Feb 2025
Viewed by 376
Abstract
Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security [...] Read more.
Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security requirements. Implicit authentication technology based on user behavior characteristics is proposed to achieve the continuous and uninterrupted authentication of savvy terminal users. This paper proposes an implicit authentication method that fuses keystroke and sensor data. To improve the accuracy of authentication, a neural network-based feature extraction model that integrates keystroke data and motion sensor data is designed. A feature space with dual-channel fusion is constructed, and a dataset collected in real scenarios is built by considering the changes in user activity scenarios and the differences in terminal holding postures. Experimental results on the collected data show that the proposed method has improved the accuracy of user authentication to a certain extent. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

25 pages, 16510 KiB  
Article
Hyperledger Fabric-Based Multi-Channel Structure for Data Exchange in Internet of Vehicles
by Yiluo Liu, Yaokai Feng and Kouichi Sakurai
Electronics 2025, 14(3), 572; https://doi.org/10.3390/electronics14030572 - 31 Jan 2025
Cited by 1 | Viewed by 1000
Abstract
The rapid growth of the Internet of Vehicles (IoV) requires secure, efficient, and reliable data exchanges among multiple stakeholders. Traditional centralized database systems can hardly address the challenges associated with data privacy, integrity, and scalability in this decentralized ecosystem. In this paper, we [...] Read more.
The rapid growth of the Internet of Vehicles (IoV) requires secure, efficient, and reliable data exchanges among multiple stakeholders. Traditional centralized database systems can hardly address the challenges associated with data privacy, integrity, and scalability in this decentralized ecosystem. In this paper, we propose a Hyperledger Fabric-Based Multi-Channel Structure to overcome these limitations. By leveraging the blockchain architecture, the system ensures data confidentiality and integrity by segregating data into exclusive channels and enabling different organizations to collaborate. Cross-channel communication ensures security when data are interacted with. Chaincodes automate transactions and enhance trust between participants. Our functional tests and performance tests by using Hyperledger Caliper verified the effectiveness of the system in real-world scenarios, highlighting its advantages over traditional systems in terms of decentralization, transparency, and security. Future work will focus on enhancing the user experience and integrating the system with edge computing. Eventually, attempts will be made to operationalize it in real-world environments. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

36 pages, 1312 KiB  
Article
Leveraging SNS Data for E-Sports Recommendation: Analyzing Popularity and User Satisfaction Metrics
by Yuanyuan Wang
Electronics 2025, 14(1), 94; https://doi.org/10.3390/electronics14010094 - 29 Dec 2024
Viewed by 1161
Abstract
The rapid rise of social media and widespread Internet access have contributed significantly to the global popularity of e-sports. However, while popular e-sports attract considerable attention, niche e-sports remain underexplored, limiting user discovery and engagement. This paper proposes a Twitter-based recommendation system that [...] Read more.
The rapid rise of social media and widespread Internet access have contributed significantly to the global popularity of e-sports. However, while popular e-sports attract considerable attention, niche e-sports remain underexplored, limiting user discovery and engagement. This paper proposes a Twitter-based recommendation system that uses advanced data management and processing techniques to address the challenge of identifying and recommending both popular and niche e-sports. The system analyzes social media metadata, including user IDs, followers, followees, engagements, and impressions, to calculate two critical metrics: popularity and satisfaction. Based on the combination of these metrics, the system calculates overall scores for each e-sports and generates two distinct rankings: one for popular and another for niche e-sports. The proposed system reflects the application of data-driven methodologies and social network analysis in creating recommendations that meet diverse user preferences, highlighting the relevance of data processing technologies in personalized content delivery. Experimental evaluations, using a dataset derived from Twitter hashtags (#) representing 30 target e-sports in 2022, demonstrate the system’s effectiveness in capturing the emerging dynamics in e-sports and providing actionable insights for diverse user preferences. This study highlights the potential of SNS-based technologies to advance data processing, analysis, and application within the e-sports ecosystem. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

18 pages, 1071 KiB  
Article
PMHR: Path-Based Multi-Hop Reasoning Incorporating Rule-Enhanced Reinforcement Learning and KG Embeddings
by Ang Ma, Yanhua Yu, Chuan Shi, Shuai Zhen, Liang Pang and Tat-Seng Chua
Electronics 2024, 13(23), 4847; https://doi.org/10.3390/electronics13234847 - 9 Dec 2024
Viewed by 1212
Abstract
Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and [...] Read more.
Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and long training and running times. In this study, we present a novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR). We address the issues of sparse rewards and spurious paths by incorporating a well-designed reward function that combines soft rewards with rule-based rewards. The rewards are adjusted based on the target entity and the path to it. Furthermore, we perform action filtering and utilize the vectors of entities and relations acquired through KG embeddings to initialize the environment, thereby significantly reducing the runtime. Experiments involving a comprehensive performance evaluation, efficiency analysis, ablation studies, and a case study were performed. The experimental results on benchmark datasets demonstrate the effectiveness of PMHR in improving KG reasoning accuracy while preserving interpretability. Compared to existing state-of-the-art models, PMHR achieved Hit@1 improvements of 0.63%, 2.02%, and 3.17% on the UMLS, Kinship, and NELL-995 datasets, respectively. PMHR provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

35 pages, 11594 KiB  
Article
Optimal Selection Technology of Business Data Resources for Multi-Value Chain Data Space—Optimizing Future Data Management Methods
by Bo Fan, Linfu Sun, Dong Tan and Meng Pan
Electronics 2024, 13(23), 4690; https://doi.org/10.3390/electronics13234690 - 27 Nov 2024
Cited by 1 | Viewed by 810
Abstract
In the field of industrial big data, the key issue in discovering data value lies not in overcoming the bottlenecks formed by analysis methods and data mining algorithms but in the difficulty of providing data element resources that meet business analysis needs. Due [...] Read more.
In the field of industrial big data, the key issue in discovering data value lies not in overcoming the bottlenecks formed by analysis methods and data mining algorithms but in the difficulty of providing data element resources that meet business analysis needs. Due to the surge in data volume and the increasing reliance of enterprises on data-driven decision-making, future data management strategies are constantly evolving to meet higher quality and efficiency requirements. Data metadata resources that meet business analysis needs require high-quality data integration, standardization, and metadata management. The key is to ensure the consistency and availability of data to support accurate analysis and decision-making. By leveraging automation and machine learning, organizations can more effectively integrate and manage data metadata resources, thereby improving data quality and analytical capabilities. The multi-value chain data space is a digital ecological platform for organizing and managing industrial big data. Research on optimizing the supply of its business data resources is a significant topic. This paper studies the evaluation index system of data quality and data utility, constructs an evaluation matrix of business data resources, and addresses the issues of data sparsity and cold start in evaluation calculations through a data quality-utility-based evaluation model of business data resources. It investigates a business data resource algorithm based on collaborative filtering, forming a recommendation set of similar data quality-utility data resources to provide to data analysis users. Finally, using actual production datasets, the paper validates the business data resource evaluation model, compares the performance and effectiveness of three business data resource recommendation algorithms based on collaborative filtering, empirically demonstrates the recommendation accuracy and stability performance of the combined improved data quality-utility collaborative filtering algorithm (CFA-DQU), and provides technical research recommendations for optimization of business data resources. Full article
(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-9
Back to TopTop