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Advanced Methods for Multi-Source Information Management, Modeling, and Analysis

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Theory and Methodology".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 14818

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Special Issue Editors

Dr. Yuan Tian

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Guest Editor

1. School of International Education, Nanjing Institute of Technology, Nanjing 211167, China
2. Waterford Institute, Nanjing University of Information Science and Technology, Nanjing 211800, China
Interests: cyber security; applied cryptography; multimedia security; privacy protection; biometrics; security management; location based service; cloud computing security
Special Issues, Collections and Topics in MDPI journals

Dr. Le Sun

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Guest Editor

School of Computer and Software, Nanjing University of Information Science and Technology, No. 219 Ningliu Road, Nanjing 210044, Jiangsu Province, China
Interests: hyperspectral remote sensing image processing (including: unmixing, classification, fusion); deep learning
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Dr. Vidyasagar Potdar

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Guest Editor

Blockchain R&D Laboratory, Curtin University, Bentley 6102, Australia
Interests: blockchain; smart grids; cyber security; wireless sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals

Dr. Biao Song

E-Mail Website
Guest Editor

School of Software, Nanjing University of Information Science and Technology, Nanjing 210000, China
Interests: information security; artificial intelligence; information protection

Special Issue Information

Dear Colleagues,

It is no exaggeration to say that our lives are shaped by multi-source information. In every aspect of our work, study, communication and consumption, we constantly create, access and utilize various kinds of data. Data can not only help us understand ourselves, others and the environment, but can also enable us to enhance efficiency, generate value, solve problems and achieve goals. Exploring advanced methods and technologies to guarantee availability and security of multi-source information is directly linked to our life and social progress.

The application of artificial intelligence and computer technology has brought people closer to data exploration. These emerging technologies can leverage data more profoundly in different scenarios, which help to deliver personalized content and services, enhance user satisfaction, improve urban management and service quality, and lower cost and risk. A common challenge that has arisen is that mining and optimizing multi-source heterogeneous data requires reliable and accurate data models as well as detailed and suitable system frameworks so as to monitor, analyze and make decisions in real time. Furthermore, it is worthwhile to actively promote the research of advanced technologies and methods to support the management of data from different sources, levels or dimensions. Additionally, for the acquisition and mining of privacy information such as user behavior and emotions, protecting the value of data information itself and the privacy and security of users is crucial.

The aim of this Special Issue is to explore various methods for multi-source information management, modeling and analysis, as well as foster a wider application of data technology in industrial engineering and various disciplines. Topics of interest include but are not limited to:

Architecture, tools and systems for multi-source data analysis;
Artificial intelligence in multi-source data mining and analysis;
Bio-inspired optimization in engineering and sciences;
Computer vision and natural language processing;
Information security, reliability, trust and privacy;
Multi-source information fusion;
Multidimensional data structures and indexing strategies;
Smart environments.

Dr. Yuan Tian
Dr. Le Sun
Dr. Vidyasagar Potdar
Dr. Biao Song
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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

data mining and analysis
engineering optimization
computer science
smart environment
information security and privacy
information fusion
artificial intelligence

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Published Papers (9 papers)

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Research

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19 pages, 2315 KB

Open AccessArticle

Client-Attentive Personalized Federated Learning for AR-Assisted Information Push in Power Emergency Maintenance

by Cong Ye, Xiao Li, Zile Lei, Jianlei Wang, Tao Zhang and Sujie Shao

Information 2025, 16(12), 1097; https://doi.org/10.3390/info16121097 - 11 Dec 2025

Cited by 1 | Viewed by 272

Abstract

The integration of AI into power emergency maintenance faces a critical dilemma: centralized training compromises privacy, while standard Federated Learning (FL) struggles with the statistical heterogeneity (Non-IID) of industrial data. Traditional aggregation algorithms (e.g., FedAvg) treat clients solely based on sample size, failing to distinguish between critical fault data and redundant normal operational data. To address this theoretical gap, this paper proposes a Client-Attentive Personalized Federated Learning (PFAA) framework. Unlike conventional approaches, PFAA introduces a semantic-aware attention mechanism driven by “Device Health Fingerprints.” This mechanism dynamically quantifies the contribution of each client not just by data volume, but by the quality and physical relevance of their model updates relative to the global optimization objective. We implement this algorithm within a collaborative cloud-edge-end architecture to enable privacy-preserving, AR-assisted fault diagnosis. Extensive simulations demonstrate that PFAA effectively mitigates model divergence caused by data heterogeneity, achieving superior convergence speed and decision accuracy compared to rule-based and standard FL baselines. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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20 pages, 1598 KB

Open AccessArticle

HGA-DP: Optimal Partitioning of Multimodal DNNs Enabling Real-Time Image Inference for AR-Assisted Communication Maintenance on Cloud-Edge-End Systems

by Cong Ye, Ruihang Zhang, Xiao Li, Wenlong Deng, Jianlei Wang and Sujie Shao

Information 2025, 16(12), 1091; https://doi.org/10.3390/info16121091 - 8 Dec 2025

Viewed by 441

Abstract

In the field of communication maintenance, Augmented Reality (AR) applications are critical for enhancing operational safety and efficiency. However, deploying the required multimodal models on resource-constrained terminal devices is challenging, as traditional cloud or on-device strategies fail to balance low latency and energy consumption. This paper proposes a Cloud-Edge-End collaborative inference framework tailored to multimodal model deployment. A subgraph partitioning strategy is introduced to systematically decompose complex multimodal models into functionally independent sub-units. Subsequently, a fine-grained performance estimation model is employed to accurately characterize both computation and communication costs across heterogeneous devices. And, a joint optimization problem is formulated to minimize end-to-end inference latency and terminal energy consumption. To solve this problem efficiently, a Hybrid Genetic Algorithm for DNN Partitioning (HGA-DP) evolved over 100 generations is designed, incorporating constraint-aware repair mechanisms and local neighborhood search to navigate the exponential search space of possible deployment combinations. Experimental results on a simulated three-tier collaborative computing platform demonstrate that, compared to traditional full on-device deployment, the proposed method reduces end-to-end inference latency by 70–80% and terminal energy consumption by 81.1%, achieving a 4.86× improvement in overall fitness score. Against the latency-optimized DADS heuristic, HGA-DP achieves 41.3% lower latency while reducing energy by 59.9%. Compared to the All-Cloud strategy, our approach delivers 71.5% latency reduction with only marginal additional terminal energy cost. This framework provides an adaptive and effective solution for real-time multimodal inference in resource-constrained scenarios, laying a foundation for intelligent, resource-aware deployment. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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24 pages, 11339 KB

Open AccessArticle

A Simulation Modeling of Temporal Multimodality in Online Streams

by Abdurrahman Alshareef

Information 2025, 16(11), 999; https://doi.org/10.3390/info16110999 - 18 Nov 2025

Viewed by 415

Abstract

Temporal variability in online streams arises in information systems where heterogeneous modalities exhibit varying latencies and delay distributions. Efficient synchronization strategies help to establish a reliable flow and ensure a correct delivery. This work establishes a formal modeling foundation for addressing temporal dynamics in streams with multimodality using a discrete-event system specification framework. This specification captures different latencies and interarrival dynamics inherent in multimodal flows. The framework also incorporates a Markov variant to account for variations in delay processes, thereby capturing timing uncertainty in a single modality. The proposed models are modular, with built-in mechanisms for diverse temporal integration, thereby facilitating heterogeneity in information flows and communication. Various structural and behavioral forms can be flexibly represented and readily simulated. The devised experiments demonstrate, across several model permutations, the time-series behavior of individual stream components and the overall composed system, highlighting performance metrics in both, quantifying composability and modular effects, and incorporating learnability into the simulation of multimodal streams. The primary motivation of this work is to enhance the degree of fitting within formal simulation frameworks and to enable adaptive, learnable distribution modeling in multimodal settings that combine synthetic and real input data. We demonstrate the resulting errors and degradation when replacing real sensor data with synthetic inputs at different dropping probabilities. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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21 pages, 1288 KB

Open AccessArticle

Intrusion Alert Analysis Method for Power Information Communication Networks Based on Data Processing Units

by Rui Zhang, Mingxuan Zhang, Yan Liu, Zhiyi Li, Weiwei Miao and Sujie Shao

Information 2025, 16(7), 547; https://doi.org/10.3390/info16070547 - 27 Jun 2025

Viewed by 774

Abstract

Leveraging Data Processing Units (DPUs) deployed at network interfaces, the DPU-accelerated Intrusion Detection System (IDS) enables microsecond-latency initial traffic inspection through hardware offloading. However, while generating high-throughput alerts, this mechanism amplifies the inherent redundancy and noise issues of traditional IDS systems. This paper proposes an alert correlation method using multi-similarity factor aggregation and a suffix tree model. First, alerts are preprocessed using LFDIA, employing multiple similarity factors and dynamic thresholding to cluster correlated alerts and reduce redundancy. Next, an attack intensity time series is generated and smoothed with a Kalman filter to eliminate noise and reveal attack trends. Finally, the suffix tree models attack activities, capturing key behavioral paths of high-severity alerts and identifying attacker patterns. Experimental evaluations on the CPTC-2017 and CPTC-2018 datasets validate the proposed method’s effectiveness in reducing alert redundancy, extracting critical attack behaviors, and constructing attack activity sequences. The results demonstrate that the method not only significantly reduces the number of alerts but also accurately reveals core attack characteristics, enhancing the effectiveness of network security defense strategies. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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17 pages, 669 KB

Open AccessArticle

Unsupervised Decision Trees for Axis Unimodal Clustering

by Paraskevi Chasani and Aristidis Likas

Information 2024, 15(11), 704; https://doi.org/10.3390/info15110704 - 5 Nov 2024

Cited by 1 | Viewed by 1815

Abstract

The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of unimodality. Unimodality is a key property indicating the grouping behavior of data around a single density mode. Our approach is based on the notion of an axis unimodal cluster: a cluster where all features are unimodal, i.e., the set of values of each feature is unimodal as decided by a unimodality test. The proposed method follows the typical top-down splitting paradigm for building axis-aligned decision trees and aims to partition the initial dataset into axis unimodal clusters by applying thresholding on multimodal features. To determine the decision rule at each node, we propose a criterion that combines unimodality and separation. The method automatically terminates when all clusters are axis unimodal. Unlike typical decision tree methods, DTAUC does not require user-defined hyperparameters, such as maximum tree depth or the minimum number of points per leaf, except for the significance level of the unimodality test. Comparative experimental results on various synthetic and real datasets indicate the effectiveness of our method. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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18 pages, 1657 KB

Open AccessArticle

Identification of Emerging Technological Hotspots from a Multi-Source Information Perspective: Case Study on Blockchain Financial Technology

by Ruiyu Hu, Zemenghong Bao, Juncheng Jia and Kun Lv

Information 2024, 15(9), 581; https://doi.org/10.3390/info15090581 - 19 Sep 2024

Cited by 2 | Viewed by 1846

Abstract

In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to accurately grasp the developmental trajectory of these tools and for their efficient utilization. In this study, we chronologically categorize information derived from five types of multi-source data, including journal articles, patent inventions, and industry reports, into distinct periods. We employ the LDA (Latent Dirichlet Allocation) topic model to identify emerging technological themes within these periods and utilize a dual-index theme lifecycle analysis method to construct a hotspot theme distribution map, thereby facilitating the extraction of significant themes. Through empirical research on blockchain financial technology, we ultimately identify 22 thematic areas of blockchain finance and extracted eight prominent themes, including financial technology, cross-border payments, digital invoices, supply chain finance, and decentralization. By analyzing these themes alongside their respective popularity levels, we validate that the methods above can be used to effectively identify emerging technological hotspots and illuminate their developmental directions. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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15 pages, 3312 KB

Open AccessArticle

Robust Mixed-Rate Region-of-Interest-Aware Video Compressive Sensing for Transmission Line Surveillance Video

by Lisha Gao, Zhoujun Ma, Shuo Han, Tiancheng Zhao, Qingcheng Liu and Zhangjie Fu

Information 2024, 15(9), 555; https://doi.org/10.3390/info15090555 - 10 Sep 2024

Viewed by 1447

Abstract

Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video compressive sensing algorithm for transmission line surveillance video compression. The proposed method compresses foreground targets and background frames separately and uses reversible neural network to reconstruct original frames. The result on transmission line surveillance video data shows that the proposed compressive sensing method can achieve 26.47, 34.71 PSNR and 0.6839, 0.9320 SSIM higher than existing methods on 1.5% and 15% measurement rates, and the proposed ROI extraction net can precisely retrieve regions under high noise levels. This research not only demonstrates the potential for a more efficient video compression technique in resource-constrained environments, but also lays a foundation for future advancements in video compressive sensing techniques and their applications in various real-time surveillance systems. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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13 pages, 2487 KB

Open AccessArticle

SiamSMN: Siamese Cross-Modality Fusion Network for Object Tracking

by Shuo Han, Lisha Gao, Yue Wu, Tian Wei, Manyu Wang and Xu Cheng

Information 2024, 15(7), 418; https://doi.org/10.3390/info15070418 - 19 Jul 2024

Cited by 1 | Viewed by 2019

Abstract

The existing Siamese trackers have achieved increasingly successful results in visual object tracking. However, the interactive fusion among multi-layer similarity maps after cross-correlation has not been fully studied in previous Siamese network-based methods. To address this issue, we propose a novel Siamese network for visual object tracking, named SiamSMN, which consists of a feature extraction network, a multi-scale fusion module, and a prediction head. First, the feature extraction network is used to extract the features of the template image and the search image, which is calculated by a depth-wise cross-correlation operation to produce multiple similarity feature maps. Second, we propose an effective multi-scale fusion module that can extract global context information for object search and learn the interdependencies between multi-level similarity maps. In addition, to further improve tracking accuracy, we design a learnable prediction head module to generate a boundary point for each side based on the coarse bounding box, which can solve the problem of inconsistent classification and regression during the tracking. Extensive experiments on four public benchmarks demonstrate that the proposed tracker has a competitive performance among other state-of-the-art trackers. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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Review

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38 pages, 4004 KB

Open AccessReview

Data Integration and Storage Strategies in Heterogeneous Analytical Systems: Architectures, Methods, and Interoperability Challenges

by Paraskevas Koukaras

Information 2025, 16(11), 932; https://doi.org/10.3390/info16110932 - 26 Oct 2025

Viewed by 3485

Abstract

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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