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Digital
Special Issues
Applications of Artificial Intelligence and Data Management in Data Analysis
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Applications of Artificial Intelligence and Data Management in Data Analysis

A special issue of Digital (ISSN 2673-6470).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8960

Special Issue Editors

Prof. Dr. Jorge Bernardino

E-Mail Website
Guest Editor
ISEC, Polytechnic University of Coimbra, Coimbra, Portugal
Interests: big data; data analytics; data management; databases; software engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Le Gruenwald

E-Mail Website
Guest Editor
School of Computer Science, University of Oklahoma, Norman, OK, USA
Interests: data mining; machine learning; data analytics; database management; information privacy and security
Special Issues, Collections and Topics in MDPI journals
Dr. Elio Masciari

E-Mail Website
Guest Editor
Department of Electric and Information Technologies, University of Naples Federico II, 80100 Naples, Italy
Interests: database; big data; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Laurent D'Orazio

E-Mail Website
Guest Editor
CNRS IRISA, Université de Rennes, Lannion, France
Interests: databases; database management systems distributed systems; parallelism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has revolutionized the field of data analysis, enabling organizations to extract valuable insights from large and complex datasets. AI algorithms and techniques can process data more efficiently, identify patterns and trends that humans might miss, and make predictions with greater accuracy.

The focus of this Special Issue is on leveraging AI techniques to enhance data management processes and extract valuable insights. It intends to explore how AI can improve data storage, retrieval, organization, and analysis, ultimately optimizing the use of data within organizations, and also how AI can enhance traditional data analysis methods, automate tasks, and uncover patterns that would be difficult or impossible for humans to identify.

The scope of this Special Issue encompasses a wide range of AI techniques and their applications across various industries. It includes, but is not limited to:

  • Machine learning: using algorithms to teach computers to learn from data and make predictions or decisions.
  • Natural language processing: analyzing and understanding human language, including text and speech.
  • Predictive analytics: forecasting future trends and outcomes based on historical data.
  • Data preprocessing and integration: using AI to clean, normalize, and prepare data for analysis and combining data from various sources into a unified dataset.
  • Data governance: implementing AI-powered tools for data quality management, compliance, and security.
  • Data warehousing and data lakes: utilizing AI to optimize the design, management, and querying of data warehouses and lakes.
  • Data visualization: creating interactive and informative visualizations using AI-powered tools.

The primary purpose of applying AI in data management is to improve the efficiency, effectiveness, and value of data analysis. By automating tasks, enhancing data quality, and facilitating data access, AI can help organizations.

The purpose of this Special Issue is to add to the body of literature and to help organizations to:

  • Make data-driven decisions: AI can provide insights and recommendations based on data analysis, enabling informed decision-making.
  • Increase efficiency and reduce costs: AI can automate manual tasks and optimize data storage and processing, leading to cost savings.
  • Improve data quality: AI can help identify and address data quality issues, ensuring data accuracy and reliability.
  • Enhance data governance: AI can automate data governance tasks, such as data classification and access control.

The purpose of this Special Issue is also to use AI to discover new opportunities to identify emerging trends and uncover hidden opportunities that might be missed by human analysis.

You may choose our Joint Special Issue in BDCC.

Prof. Dr. Jorge Bernardino
Prof. Dr. Le Gruenwald
Dr. Elio Masciari
Prof. Dr. Laurent D'Orazio
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. Digital is an international peer-reviewed open access quarterly 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 1200 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

  • artificial intelligence (AI)
  • data management
  • data analysis
  • machine learning
  • data governance
  • predictive analytics
  • data quality
  • data visualization
  • data mining

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  • 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 (4 papers)

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Research

18 pages, 499 KB  
Article
Early Anomaly Detection in Shrimp Pond Water Quality Using Supervised and Unsupervised Machine Learning Models
by Hamilton Villamar-Barros, Julián Coronel-Reyes and Alexander Haro-Sarango
Digital 2026, 6(2), 27; https://doi.org/10.3390/digital6020027 - 1 Apr 2026
Viewed by 415
Abstract
Shrimp aquaculture increasingly depends on precise water quality management, yet most farms still rely on fragmented measurements and qualitative assessments. This study aimed to evaluate whether routine physicochemical data from commercial ponds can reliably discriminate between operational categories of acceptable and residual water [...] Read more.
Shrimp aquaculture increasingly depends on precise water quality management, yet most farms still rely on fragmented measurements and qualitative assessments. This study aimed to evaluate whether routine physicochemical data from commercial ponds can reliably discriminate between operational categories of acceptable and residual water and thus support early warning systems. We compiled water quality records from shrimp ponds in several coastal provinces, focusing on a reduced set of variables related to salinity, alkalinity, hardness and inorganic nitrogen. Supervised and unsupervised machine learning models were trained and compared using standard classification metrics. Tree-based ensembles and margin-based models achieved high accuracy and F1 scores when predicting water status from routine variables, while clustering methods only reproduced similar patterns after an ex post mapping of clusters to classes. These results indicate that latent nitrogen loads and subtle shifts in water chemistry are systematically captured by basic monitoring data and can be translated into operational signals of risk. The study demonstrates the feasibility of integrating data-driven classification into shrimp farm monitoring and outlines a pathway toward low-cost, scalable decision support tools for aquaculture 4.0 in data-limited settings. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Data Management in Data Analysis)
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25 pages, 3590 KB  
Article
Unlocking Innovation in Tourism: A Bibliometric Analysis of Blockchain and Distributed Ledger Technology Trends, Hotspots, and Future Pathways
by Roberto A. Pava-Díaz, Juan M. Sánchez-Céspedes and Oscar Danilo Montoya
Digital 2026, 6(1), 7; https://doi.org/10.3390/digital6010007 - 19 Jan 2026
Viewed by 530
Abstract
This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize [...] Read more.
This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize the evolution, structure, and thematic focus of this emerging field of research. The systematic literature review, which adhered to PRISMA guidelines, involved retrieving publications from the Web of Science and Scopus databases. A curated dataset of 100 relevant documents was identified and analyzed in terms of annual scientific production, leading journals, influential authors, and highly cited publications. The results indicate that blockchain technology dominates the literature, with a strong emphasis on its potential to enhance trust, transparency, and efficiency in tourism-related processes. In particular, identity management, secure transactions, and disintermediation emerge as central research themes, reflecting blockchain’s capacity to support decentralized, immutable, and privacy-preserving interactions between tourists and service providers. Overall, the findings reveal a rapidly growing and increasingly structured body of knowledge, highlighting emerging research directions and technological challenges for future studies on DLT applications in tourism. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Data Management in Data Analysis)
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16 pages, 2733 KB  
Article
APOLLO: Autonomous Predictive On-Chain Learning Orchestrator for AI-Driven Blockchain Governance
by Istiaque Ahmed, Zubaer Mahmood Zubraj, Md Sadek Ferdous, Tadashi Nakano and Thi Hong Tran
Digital 2026, 6(1), 3; https://doi.org/10.3390/digital6010003 - 29 Dec 2025
Viewed by 1370
Abstract
Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order [...] Read more.
Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Data Management in Data Analysis)
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14 pages, 2752 KB  
Article
TinyML Classification for Agriculture Objects with ESP32
by Danila Donskoy, Valeria Gvindjiliya and Evgeniy Ivliev
Digital 2025, 5(4), 48; https://doi.org/10.3390/digital5040048 - 2 Oct 2025
Cited by 1 | Viewed by 5328
Abstract
Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost [...] Read more.
Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Data Management in Data Analysis)
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