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Applied Sciences
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Digitalization, Information Systems and Artificial Intelligence in Business Processing
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Digitalization, Information Systems and Artificial Intelligence in Business Processing

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

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 3928

Special Issue Editors

Dr. Julia Gallardo-Pérez

E-Mail Website
Guest Editor
Department of Financial Economics, Accounting and Operations Management, University of Huelva, 21071 Huelva, Spain
Interests: ICT business; ICT tourism; ICT education
Prof. Dr. Alfonso Infante-Moro

E-Mail Website
Guest Editor
Department of Financial Economics, Accounting and Operations Management, University of Huelva, 21071 Huelva, Spain
Interests: specialized in information systems and technologies; ICT business; ICT tourism; ICT education
Special Issues, Collections and Topics in MDPI journals
Dr. Juan C. Infante-Moro

E-Mail Website
Guest Editor
Department of Financial Economics, Accounting and Operations Management, University of Huelva, 21071 Huelva, Spain
Interests: specialized in information systems and technologies; ICT business; ICT tourism; ICT education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intersection of Business Intelligence (BI) and Business Process Management (BPM) has become increasingly crucial in today's data-driven business environment. As organizations strive to enhance efficiency, decision-making, and competitive advantage, the integration of BI and BPM offers powerful solutions to optimize processes, improve operational performance, and foster innovation. This Special Issue delves into the theoretical foundations and practical applications of BI and BPM, exploring how data-driven insights can be leveraged to transform business operations.

We are pleased to invite you to contribute to this Special Issue, which is titled “Digitalization, Information Systems and Artificial Intelligence in Business Processing”. This Special Issue will serve as a comprehensive resource for researchers, practitioners, and industry professionals seeking to understand and leverage the synergies between BI and BPM.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Integration of BI and BPM;
  2. Data Analytics and Process Optimization to enhance business processes;
  3. Process Mining and Workflow Automation;
  4. Real-time Decision Support Systems, i.e., the development and implementation of systems that provide real-time analytics and insights for decision-making;
  5. AI and Machine Learning in BI and BPM;
  6. Technological Advancements, i.e., the exploration of new technologies driving the evolution of BI and BPM.
  7. We look forward to receiving your contributions.

Dr. Julia Gallardo-Pérez
Prof. Dr. Alfonso Infante-Moro
Dr. Juan C. Infante-Moro
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. 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

  • business intelligence
  • data analytics
  • process mining
  • real-time decision support
  • artificial intelligence
  • machine learning
  • ICT business
  • ICT tourism
  • ICT education

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Published Papers (3 papers)

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Research

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28 pages, 1384 KB  
Article
A Framework for a Public Service Recommender System Based on Neuro-Symbolic AI
by Ioannis Konstantinidis, Ioannis Magnisalis and Vassilios Peristeras
Appl. Sci. 2025, 15(20), 11235; https://doi.org/10.3390/app152011235 - 20 Oct 2025
Viewed by 1924
Abstract
Public service provision is still limited to document-centric procedures that require citizens to submit data and information needed for the execution of a service via documents. This, amongst others, is time-consuming, error-prone and hinders progress towards data-centricity. This study proposes a data-centric framework [...] Read more.
Public service provision is still limited to document-centric procedures that require citizens to submit data and information needed for the execution of a service via documents. This, amongst others, is time-consuming, error-prone and hinders progress towards data-centricity. This study proposes a data-centric framework for a public service recommender system that combines knowledge graphs (KGs) and large language models (LLMs) in a neuro-symbolic AI architecture. The framework expresses public service preconditions as machine-readable rules based on data standards and provides dynamic recommendations for public services based on citizens’ profiles through automated reasoning. LLMs are utilized to extract preconditions from unstructured textual regulations and create RDF-based evidence models, while KGs provide validation of preconditions through SHACL rules and explainable reasoning towards semantic interoperability. A prototype use case on students applying for housing allowance showcases the feasibility of the proposed framework. The analysis indicates that combining KGs with LLMs for identifying relevant public services for different citizens’ profiles can improve the quality of public services and reduce administrative burdens. This work contributes and promotes the proactive “No-Stop Government” model, where services are recommended to users without explicit requests. The findings highlight the promising potential of employing neuro-symbolic AI to transform e-government processes, while also addressing challenges related to legal complexity, privacy and data fragmentation for large-scale adoption. Full article
(This article belongs to the Special Issue Digitalization, Information Systems and Artificial Intelligence in Business Processing)
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28 pages, 8791 KB  
Article
CRSensor: A Synchronized and Impact-Aware Traceability Framework for Business Application Development
by Soojin Park
Appl. Sci. 2025, 15(20), 11083; https://doi.org/10.3390/app152011083 - 16 Oct 2025
Viewed by 763
Abstract
To enable effective change impact management in business applications, robust requirements traceability is essential. However, manual approaches are inefficient and prone to errors. While the prior Model-Driven Engineering (MDE)-based research, including the author’s theoretical models, established the principles of traceability, these approaches lacked [...] Read more.
To enable effective change impact management in business applications, robust requirements traceability is essential. However, manual approaches are inefficient and prone to errors. While the prior Model-Driven Engineering (MDE)-based research, including the author’s theoretical models, established the principles of traceability, these approaches lacked decisive quantitative validation using metrics such as precision and recall, thereby limiting their real-world applicability. This paper addresses these limitations by introducing the CRSensor framework, which integrates the real-time automated trace link generation and dynamic refinement of the developer model. This approach enhances the reliability and completeness of organizational impact analysis, resolving key weaknesses of conventional link recovery methods. Notably, CRSensor maintains structural consistency throughout the lifecycle, overcoming reliability limitations often found in traditional information retrieval (IR)/machine learning (ML)-based traceability solutions. Empirical evaluation demonstrates that CRSensor achieves an average trace link setting performance with a precision of 0.95, a recall of 0.98, and an auto-generation rate of 80%. These results validate both the industrial applicability and the quantitative rigor of the proposed framework, paving the way for broader practical adoption. Full article
(This article belongs to the Special Issue Digitalization, Information Systems and Artificial Intelligence in Business Processing)
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Other

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20 pages, 1940 KB  
Systematic Review
Virtual Reality for Pain and Anxiety Management During Medical Procedures: A Systematic Review with Complementary Bibliometric Analysis
by Daniel Fernández Cerero, Marta Montenegro Rueda and José Fernández Cerero
Appl. Sci. 2026, 16(9), 4193; https://doi.org/10.3390/app16094193 - 24 Apr 2026
Viewed by 170
Abstract
Virtual Reality (VR) has emerged as a non-pharmacological intervention for managing pain and anxiety during medical procedures. This study presents a systematic review with complementary bibliometric analysis of the scientific literature on the clinical effectiveness of VR in healthcare settings. A structured search [...] Read more.
Virtual Reality (VR) has emerged as a non-pharmacological intervention for managing pain and anxiety during medical procedures. This study presents a systematic review with complementary bibliometric analysis of the scientific literature on the clinical effectiveness of VR in healthcare settings. A structured search was conducted across five databases (Web of Science (WoS), Scopus, PubMed, EMBASE, and MEDLINE), identifying 627 records, of which 26 studies met the inclusion criteria. Data were extracted on study design, population, type of intervention, and clinical outcomes related to pain and anxiety. Most included studies reported reductions in perceived pain and/or anxiety when VR was used as an adjunctive intervention, particularly in pediatric and procedural contexts. However, findings were heterogeneous in terms of study design, VR modalities, and outcome measures, limiting quantitative synthesis. The bibliometric analysis indicates growing research interest, with a strong focus on clinical outcomes, while evidence related to implementation and healthcare system integration remains limited. Overall, VR appears to be a promising complementary tool for improving patient experience during medical procedures. However, further high-quality studies with standardized methodologies are needed to establish its effectiveness and facilitate future meta-analyses. Full article
(This article belongs to the Special Issue Digitalization, Information Systems and Artificial Intelligence in Business Processing)
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