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Electronics
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Advances in Web Data Management
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Advances in Web Data Management

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 2481

Editor

Prof. Dr. Kalpdrum Passi

E-Mail Website
Guest Editor
Department of Mathematics & Computer Science, Laurentian University, Sudbury, ON, Canada
Interests: web data management; XML data management; social networks; semantic web; ontologies; peer-to-peer systems; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the digital era, web data management has undergone a remarkable evolution, driven by the explosive growth of data volume, variety, and velocity on the web. Today, it extends far beyond traditional database management to encompass the acquisition, integration, processing, storage, and analysis of vast, heterogeneous, and dynamic data from diverse web sources. This Special Issue, titled “Advances in Web Data Management”, is dedicated to presenting cutting-edge research and developments within this vibrant field.

This Special Issue solicits high-calibre research articles, comprehensive review papers, and concise short communications. The scope of interest encompasses, but is not restricted to, the following areas:

  1. XML Data Management: This includes advanced XML query languages, the evolution of XML schemas, and XML-based data integration methodologies.
  2. Semantic Web Technologies: Areas such as ontology development, semantic annotation, and reasoning engines are of interest.
  3. Web Data Mining: The extraction of valuable insights from web-sourced data, including social network analysis and sentiment analysis, is a key focus.
  4. Privacy-Preserving Management: Techniques such as differential privacy, federated learning, and secure multi-party computation are being integrated into web data management systems.
  5. Scalable Processing Engines: The maturation of distributed computing frameworks and hybrid transactional/analytical processing systems enables complex queries and analytics on live, operational data at web scale.
  6. Enhanced Data Quality and Provenance: Automated data cleaning, validation, and trust assessment will be addressed. Blockchain-inspired techniques are being explored for immutable data provenance, tracking the origin, ownership, and transformation history of web data.

We are confident that this Special Issue will make a substantial contribution to both the academic and industrial communities. It will serve as a platform for the dissemination of innovative ideas and research outcomes, while also fostering collaborations between researchers and practitioners, thereby propelling the future development of web data management.

Prof. Dr. Kalpdrum Passi
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • web data management
  • XML technology
  • semantic web
  • ontology engineering
  • linked data
  • data integration
  • web application development
  • knowledge representation

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Published Papers (4 papers)

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Research

31 pages, 349 KB  
Article
Crisis Disinformation and Verification Dynamics in the València 2024 DANA
by Juan José Climent-Ferrer, J. Ernesto Solanes, Ana Martí-Testón, Flavio Moriniello, Adolfo Muñoz and Luis Gracia
Electronics 2026, 15(11), 2358; https://doi.org/10.3390/electronics15112358 - 29 May 2026
Viewed by 381
Abstract
This study examines the circulation of disinformation during the 2024 València DANA (high-altitude isolated depression), which produced torrential rainfall across eastern and southern Spain between 29 and 31 October 2024. Using a quantitative content analysis, it analyzes the 100 most viral false or [...] Read more.
This study examines the circulation of disinformation during the 2024 València DANA (high-altitude isolated depression), which produced torrential rainfall across eastern and southern Spain between 29 and 31 October 2024. Using a quantitative content analysis, it analyzes the 100 most viral false or misleading claims, classifying them by typology, format, dissemination channel, and narrative strategy. Findings show an ecosystem dominated by conspiracy narratives about the causes of the disaster and by audiovisual content—particularly short videos and images—which achieved substantially greater reach than textual posts. Narrative mechanisms such as decontextualization, emotional appeal, and political polarization were recurrent and often combined. Verification efforts that matched the original format were associated with higher relative correction reach, although their observable diffusion remained lower than that of the false claims in the analyzed sample. Overall, the study highlights the cross-platform and multimodal dynamics of crisis disinformation and underscores the need for proactive, technologically supported communication strategies. These include automated monitoring, multimodal verification, and interoperable digital infrastructures for crisis communication. Full article
(This article belongs to the Special Issue Advances in Web Data Management)
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24 pages, 1278 KB  
Article
A Study on a Network Intrusion Detection System Based on the Fusion of SAGEConv-GNN and a Transformer Encoder
by Hoang Duc Binh, Yong-ha Choi, Jaeyeong Jeong, Yong-Joon Lee and Dongkyoo Shin
Electronics 2026, 15(8), 1737; https://doi.org/10.3390/electronics15081737 - 20 Apr 2026
Cited by 1 | Viewed by 705
Abstract
A network intrusion detection system (NIDS) plays a critical role in protecting modern networked environments, but conventional approaches often struggle to balance the detection of previously unseen attacks with a low false alarm rate. This study proposes a hybrid intrusion detection model, HybridSAGETransformerGlobal, [...] Read more.
A network intrusion detection system (NIDS) plays a critical role in protecting modern networked environments, but conventional approaches often struggle to balance the detection of previously unseen attacks with a low false alarm rate. This study proposes a hybrid intrusion detection model, HybridSAGETransformerGlobal, which integrates a SAGEConv-based graph neural network (GNN) and a Transformer encoder to jointly learn local structural information and global contextual dependencies from network traffic. In the proposed framework, network flows are represented as graph nodes, and edges are constructed using IP-group-aware k-nearest neighbors (KNNs) together with a temporal chain. The model further incorporates a gated fusion mechanism, multiple positional encodings, class weighting, label smoothing, and early stopping to improve training stability and detection performance. The proposed method was evaluated under a unified preprocessing and training pipeline on two benchmark datasets, UNSW-NB15 and CIC-IDS2017, using up to approximately 100,000 flow samples per dataset, and was compared with GCN, GAT, GraphSAGE, and a Transformer-only baseline. On UNSW-NB15, repeated-run evaluation over five random seeds showed that the proposed model achieved an accuracy of 0.9841 ± 0.0006, a macro-precision of 0.9684 ± 0.0010, a macro-recall of 0.9818 ± 0.0026, and a macro-F1-score of 0.9749 ± 0.0011, with statistically significant improvements over the strongest baseline in the macro-F1-score. On CIC-IDS2017, the proposed hybrid model also showed consistently strong performance, achieving an accuracy of 0.9749, a macro-precision of 0.9513, a macro-recall of 0.9722, a macro-F1-score of 0.9613, and an ROC-AUC of 0.9957. Additional ablation, sensitivity, and baseline re-optimization analyses further supported the robustness of the proposed design. These results suggest that a coordinated hybrid architecture combining structural graph learning and long-range contextual modeling can provide an effective framework for robust flow-based network intrusion detection under the evaluated settings. Full article
(This article belongs to the Special Issue Advances in Web Data Management)
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24 pages, 1664 KB  
Article
Optimizing Influence Maximization in Social Networks via Centrality-Driven Discrete Particle Swarm Optimization (DPSO)
by John Titos Papadakis and Haridimos Kondylakis
Electronics 2026, 15(8), 1730; https://doi.org/10.3390/electronics15081730 - 19 Apr 2026
Viewed by 521
Abstract
Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the [...] Read more.
Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the IM problem is NP-hard, making exact solutions computationally infeasible for large-scale networks. Existing approximation methods typically rely either on static centrality heuristics, which often ignore global network structure, or on metaheuristic algorithms, which may suffer from slow convergence due to random initialization. This paper proposes a novel approach, termed Advanced Centrality-Driven Discrete Particle Swarm Optimization (DPSO), which integrates a weighted hybrid centrality score combining Degree, PageRank, and Betweenness centrality to guide the stochastic search process. In addition, a systematic grid search methodology is employed to determine the optimal weight configuration of the hybrid score. Experiments conducted on three real-world datasets (Twitter, ego-Facebook, and ca-HepTh) demonstrate that the optimal seeding strategy is strongly dependent on network topology. The results show that dense social networks favor popularity-based metrics such as Degree and PageRank, whereas sparse collaboration networks benefit significantly from bridge-oriented metrics such as Betweenness centrality. Overall, the proposed method achieves consistent improvements in influence spread across different network types, with the largest gains (up to 70%) observed in sparse network settings. Full article
(This article belongs to the Special Issue Advances in Web Data Management)
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18 pages, 1551 KB  
Article
Enhancing Recommendation with Integration of Extractive and Abstractive Summarization
by Minkyung Park, Suji Kim, Xinzhe Li, Seonu Park and Jaekyeong Kim
Electronics 2026, 15(7), 1477; https://doi.org/10.3390/electronics15071477 - 1 Apr 2026
Viewed by 536
Abstract
With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full [...] Read more.
With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full review texts, which may contain redundant semantics or noise that is irrelevant to recommendations, thereby degrading data quality and recommendation performance. To address this limitation, this study proposes summarized reviews fusion for adaptive recommendation (SuReFAR), which predicts ratings by summarizing reviews into key information using a multi-summarization strategy. Specifically, SuReFAR utilizes TextRank and bidirectional and auto-regressive transformers (BART) to generate extractive and abstractive summaries of user and item review sets, respectively. Subsequently, we apply an attention mechanism to emphasize salient information within each summary representation and fuse multiple summary representations by adaptively controlling their contributions through a gated multimodal unit (GMU) to predict ratings. We conducted experiments on Amazon and Yelp review datasets, demonstrating that the proposed model consistently outperforms baseline models and captures user preferences more effectively via personalized summary representations. Full article
(This article belongs to the Special Issue Advances in Web Data Management)
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