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Systems
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Data Integration and Governance in Business Intelligence Systems
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Data Integration and Governance in Business Intelligence Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3568

Special Issue Editors

Dr. Bruno Oliveira

E-Mail Website
Guest Editor
CIICESI, School of Management and Technology, Porto Polytechnic, 4610-156 Felgueiras, Portugal
Interests: data warehouse; ETL/data integration; data quality; data analytics and business intelligence
Dr. Óscar Oliveira

E-Mail Website
Guest Editor
CIICESI, School of Management and Technology, Porto Polytechnic, 4610-156 Felgueiras, Portugal
Interests: optimization; ETL/data integration; data quality
Dr. Telmo Matos

E-Mail Website
Guest Editor
CIICESI, School of Management and Technology, Porto Polytechnic, 4610-156 Felgueiras, Portugal
Interests: business intelligence; data visualization; operation research

Special Issue Information

Dear Colleagues,

Data governance refers to the sets of practices, policies, and procedures used to improve data quality, integrity, and security. As organizations and individuals continue to produce, share, and leverage vast amounts of data, the importance of robust data governance cannot be overstated. From personal information to organizational assets, data have come to constitute a critical resource driving innovation and decision making. This growth raises significant challenges, not only in data usage but also in related processing and storage processes, making effective data governance more crucial than ever. Data governance provides a framework to organize the data that are integrated, transformed, and used for business intelligence activities.

This Special Issue invites scientific contributions proposing new, innovative, and original approaches for the development of business intelligence using data governance practices. This Issue aims to provide an opportunity for academics and practitioners to share their theoretical and practical knowledge and findings in the field.

This Special Issue particularly looks forward to articles presenting, among others:

  • Artificial intelligence applied to business intelligence and data governance.
  • Business intelligence, business analytics and data integration.
  • Data catalogs and active metadata.
  • Semantic data and knowledge graphs.
  • Inception and development of modern data architectures for data lakes, data warehouses, data lakehouses, data meshes and vaults.
  • Data quality, curation provenance, security and their role in business intelligence systems.
  • Innovative approaches and challenges for data integration.
  • Data governance applications for learning, finance, marketing, banking, medicine, industry and services.

Dr. Bruno Oliveira
Dr. Óscar Oliveira
Dr. Telmo Matos
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 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. Systems 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 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 quality
  • data integration
  • business analytics
  • data governance
  • business intelligence
  • data architectures

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

Published Papers (3 papers)

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Research

28 pages, 984 KiB  
Article
Data Quality Assessment in Smart Manufacturing: A Review
by Teresa Peixoto, Bruno Oliveira, Óscar Oliveira and Fillipe Ribeiro
Systems 2025, 13(4), 243; https://doi.org/10.3390/systems13040243 - 31 Mar 2025
Viewed by 212
Abstract
Data quality in IoT and smart manufacturing environments is essential for optimizing workflows, enabling predictive maintenance, and supporting informed decisions. However, data from sensors present significant challenges due to their real-time nature, diversity of formats, and high susceptibility to faults such as missing [...] Read more.
Data quality in IoT and smart manufacturing environments is essential for optimizing workflows, enabling predictive maintenance, and supporting informed decisions. However, data from sensors present significant challenges due to their real-time nature, diversity of formats, and high susceptibility to faults such as missing values or inconsistencies. Ensuring high-quality data in these environments is crucial to maintaining operational efficiency and process reliability. This paper analyzes some of the data quality metrics presented in the literature, with a focus on adapting them to the context of Industry 4.0. Initially, three models for the classification of the dimensions of data quality are presented, proposed by different authors, which group together dimensions such as accuracy, completeness, consistency, and timeliness in different approaches. Next, a systematic methodology is adopted to evaluate the metrics related to these dimensions, always using a real-time monitoring scenario. This approach combines dynamic thresholds with historical data to assess the quality of incoming data streams and provide relevant insights. The analysis carried out not only facilitates continuous monitoring of data quality but also supports informed decision-making, helping to improve operational efficiency in Industry 4.0 environments. Finally, this paper presents a table summarizing the selected metrics, highlighting the advantages, disadvantages, and potential usage scenarios, and providing a practical basis for implementation in real environments. Full article
(This article belongs to the Special Issue Data Integration and Governance in Business Intelligence Systems)
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29 pages, 7928 KiB  
Article
A Tripartite Evolutionary Game Analysis of Enterprise Data Sharing Under Government Regulations
by Ying Dong, Zhongyuan Sun and Luyi Qiu
Systems 2025, 13(3), 151; https://doi.org/10.3390/systems13030151 - 24 Feb 2025
Viewed by 479
Abstract
The tripartite evolutionary game model focuses on the strategic choices and evolutionary laws of three parties in dynamic interaction. By constructing a tripartite evolutionary game model involving the government, Enterprise A, and Enterprise B, this paper analyzes the strategic choices of enterprise data [...] Read more.
The tripartite evolutionary game model focuses on the strategic choices and evolutionary laws of three parties in dynamic interaction. By constructing a tripartite evolutionary game model involving the government, Enterprise A, and Enterprise B, this paper analyzes the strategic choices of enterprise data sharing from the perspective of government regulation and uses the simulation method to assign and simulate the parameters of the model. Furthermore, the evolutionary trends of the behavioral strategies of the three parties are analyzed under the changes of factors such as the government’s regulation costs, government penalties, government rewards, and the compensation fees for enterprises to obtain shared data. The findings indicate that when the benefits obtained by enterprises from data sharing are relatively high, and the compensation fees incurred by enterprises to obtain the other party’s data are sufficient to compensate for the losses caused by the other party’s data sharing, enterprises will tend to choose “data-sharing”. At this time, the combined strategy of “no-regulation, data-sharing, data-sharing” reaches an equilibrium point. In this combination strategy, the initial willingness of the government and enterprises will not affect the final evolutionary result. The government’s regulation costs, government penalties, and government rewards will not affect the final behavioral strategy evolutionary result for the government and enterprises. However, the compensation fees for enterprises to obtain shared data will affect the final evolutionary direction of the three parties. When the compensation fees for enterprises to obtain shared data are low, enterprises are more inclined toward “no-data-sharing”. Full article
(This article belongs to the Special Issue Data Integration and Governance in Business Intelligence Systems)
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21 pages, 3529 KiB  
Article
Evaluating User Engagement in Online News: A Deep Learning Approach Based on Attractiveness and Multiple Features
by Guohui Song, Yongbin Wang, Xiaosen Chen, Hongbin Hu and Fan Liu
Systems 2024, 12(8), 274; https://doi.org/10.3390/systems12080274 - 30 Jul 2024
Viewed by 2079
Abstract
Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs [...] Read more.
Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation. Full article
(This article belongs to the Special Issue Data Integration and Governance in Business Intelligence Systems)
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