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Information
Special Issues
Editorial Board Members’ Collection Series: "Information Systems"
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Editorial Board Members’ Collection Series: "Information Systems"

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1190

Special Issue Editors

Prof. Dr. Sokratis Katsikas

E-Mail Website
Guest Editor
Department of Information Security and Communication Technology, Faculty of Information Technology and Electrical Engineering, NTNU, N-2815 Gjøvik, Norway
Interests: cybersecurity; adversarial machine learning; cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Moscato

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: social network analytics; multimedia recommender systems; big data; artificial intelligence; graph mining; IoT; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Information System Collection series aims to publish original research and review papers on all areas of information system development, data management, business intelligence, digital technologies, information systems security and privacy, etc.

Specifically, the scope of the Editorial Board Member Collection Series includes, but is not limited to, the following:

  • Foundational Theory and Methods of Information Systems;
  • Information Systems Development;
  • Data Management and Analytics;
  • Information Systems Security and Privacy;
  • Business Intelligence and Decision Support;
  • Cloud Computing and Service Computing;
  • Intelligent Systems and Information Management;
  • Intelligent Transportation Systems;
  • Health Information Systems.

Prof. Dr. Sokratis Katsikas
Dr. Vincenzo Moscato
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. 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 1600 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

  • information systems
  • data management
  • information systems security and privacy
  • information management

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

Published Papers (1 paper)

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Research

26 pages, 3721 KiB  
Article
Schema Understandability: A Comprehensive Empirical Study of Requirements Metrics
by Tanu Singh, Vinod Patidar, Manu Singh and Álvaro Rocha
Information 2025, 16(2), 155; https://doi.org/10.3390/info16020155 - 19 Feb 2025
Viewed by 1036
Abstract
Ensuring high-quality data warehouses is crucial for organizations, as they provide the reliable information needed for informed decision-making. While various methodologies emphasize the importance of requirements, conceptual, logical, and physical models in developing data warehouses, empirical quality assessment of these models remains underexplored, [...] Read more.
Ensuring high-quality data warehouses is crucial for organizations, as they provide the reliable information needed for informed decision-making. While various methodologies emphasize the importance of requirements, conceptual, logical, and physical models in developing data warehouses, empirical quality assessment of these models remains underexplored, especially requirements models. To bridge this gap, this study focuses on assessment of requirements metrics for predicting the understandability of requirements schemas, a key indicator of model quality. In this empirical study, 28 requirements schemas were classified into understandable and non-understandable clusters using the k-means clustering technique. The study then employed six classification techniques—logistic regression, naive Bayes, linear discriminant analysis with decision tree, reinforcement learning, voting rule, and a hybrid approach—within both univariate and multivariate models to identify strong predictors of schema understandability. Results indicate that 13 out of 17 requirements metrics are robust predictors of schema understandability. Furthermore, a comparative performance analysis of the classification techniques reveals that the hybrid classifier outperforms other techniques across key evaluation parameters, including accuracy, sensitivity, specificity, and AUC. These findings highlight the potential of requirements metrics as effective predictors of schema understandability, contributing to improved quality assessment and the development of better conceptual data models for data warehouses. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Systems")
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