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“Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 53539

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Prof. Dr. Mirjana Ivanović

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Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 4, 21000 Novi Sad, Serbia
Interests: intelligent tutoring systems; personalized learning environments; application of AI techniques; agent technologies; machine learning; gender aspects in computer science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Richard Chbeir

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Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France
Interests: data management; semantics; digital ecosystems
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Prof. Dr. Yannis Manolopoulos

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Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2427 Nicosia, Cyprus
Interests: data management; data mining; data science; scientometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the pioneers of computer science recently passed away: William I. Grosky (or Bill for friends). He received his B.S. degree in Mathematics from MIT in 1965, his M.S. degree in Applied Mathematics from Brown University in 1968, and his Ph.D. degree from Yale University in 1971. William I. Grosky served as a Professor and Chair of the Department of Computer and Information Science at the University of Michigan-Dearborn (UMD). Before joining UMD in 2001, he was the Professor and Chair of the Department of Computer Science at Wayne State University, as well as an Assistant Professor of Information and Computer Science at the Georgia Institute of Technology in Atlanta. His research interests included multimedia information systems, text and image mining, and the semantic web. He was a founding member of Intelligent Media LLC, a Michigan-based company, whose interests are in integrating new media into information technologies. He delivered many short courses in the area of database management for local industries and was invited to lecture on multimedia information systems worldwide. He served as the Editor-in-Chief of the IEEE Multimedia magazine, as well as the Member of Editorial Boards of many journals in the field; in addition, he served as a Member of several Program Committees of conferences focusing on database and multimedia systems. He published 3 books and more than 150 papers in international conferences and journals.

His peers, students and friends, and our community remember him not only as an excellent scientist, but also as a very generous, funny, and curious person, passionately delivering lectures and seminars. When one needed to brainstorm any new technologies or ideas, he was the person to speak to. His impressive knowledge in Computer Science always allowed for constructive and enriching conversations. He used to state that one of the luxuries of academia is the ability to learn every day.

We, therefore, felt the need and duty to collect a series of papers by his students, friends, and colleagues, compiled in this Special Issue dedicated to Prof. William I. Grosky, for which we are interested in article topics addressing a broad scope, thereby paying tribute to the rich scientific curiosity of Prof. Grosky.

In the world of the Internet of Things (IoT), the rapid growth and exponential use of digital components has led to the emergence of intelligent connected environments, composed of multiple independent entities such as individuals, organizations, services, software, and applications, sharing one or several missions, focusing on the interactions and inter-relationships among them. The application of information technologies has the potential to enable the understanding of how entities request resources and, ultimately, interact to create benefits and added values, impacting business practices and knowledge. These technologies can be improved through novel techniques, models, and methodologies for fields such as big data management, web technologies, networking, security, human–computer interactions, artificial intelligence, e-services, and self-organizing systems, supporting the establishment of digital ecosystems and management of their resources.

The phenomena of collective intelligence in connected environments have emerged where i) the diversity and plenitude of shared resources and ii) users act both as content consumers and content providers. How can we make the most out of these vast amounts of easily searchable resources, capable of inferring new information and knowledge? Recent research advances have stimulated the development of a series of innovative approaches, algorithms, and tools for concept/topic detection or extraction, respectively.

This Special Issue invites high-quality research papers describing researchers’ latest results in the challenges of integrating, enriching, and consuming resources in connected environments. This Special Issue seeks contributions in the following areas:

Digital Ecosystem Infrastructure;
Data and Knowledge Management;
Computational and Collective Intelligence;
Semantic Computing;
Software Ecosystems for Software Engineering;
Big Data;
Services;
Trust, Security, and Privacy;
Software Engineering;
Internet of Things and Intelligent Web;
Cyber Physical Systems;
Social and Collaborative Platforms;
Human–computer Interaction;
Open Source;
Complex Systems and Networks;
Applications (Logistics, Energy, Healthcare, Environment, Smart Cities, Digital Humanities, Robotics, etc.).

Prof. Dr. Mirjana Ivanović
Prof. Dr. Richard Chbeir
Prof. Dr. Yannis Manolopoulos
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. 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 1000 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.

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Published Papers (12 papers)

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12 pages, 1967 KiB

Open AccessArticle

Quality Control Methods Using Quality Characteristics in Development and Operations

by Daiju Kato and Hiroshi Ishikawa

Digital 2024, 4(1), 232-243; https://doi.org/10.3390/digital4010012 - 1 Mar 2024

Cited by 1 | Viewed by 1439

Abstract

Since the Software Quality Model was defined as an international standard, many quality assurance teams have used this quality model in a waterfall model for software development and quality control. As more software is delivered as a cloud service, various methodologies have been created with an awareness of the link between development productivity and operations, enabling faster development. However, most development methods are development-oriented with awareness of development progress, and there has been little consideration of methods that achieve quality orientation for continuous quality improvement and monitoring. Therefore, we developed a method to visualize the progress of software quality during development by defining quality goals in the project charter using the quality model defined in international standards, classifying each test by quality characteristics, and clarifying the quality ensured by each test. This was achieved by classifying each test by quality characteristics and clarifying the quality ensured by each test. To use quality characteristics as KPIs, it is necessary to manage test results for each test type and compare them with past build results. This paper explains how to visualize the quality to be assured and the benefits of using quality characteristics as KPIs and proposes a method to achieve rapid and high-quality product development. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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17 pages, 3413 KiB

Open AccessArticle

Object Detection Models and Optimizations: A Bird’s-Eye View on Real-Time Medical Mask Detection

by Dimitrios A. Koutsomitropoulos and Ioanna C. Gogou

Digital 2023, 3(3), 172-188; https://doi.org/10.3390/digital3030012 - 1 Jul 2023

Cited by 1 | Viewed by 1838

Abstract

Convolutional Neural Networks (CNNs) are well-studied and commonly used for the problem of object detection thanks to their increased accuracy. However, high accuracy on its own says little about the effective performance of CNN-based models, especially when real-time detection tasks are involved. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of their speed/accuracy trade-off. This work performs a review and hands-on evaluation of the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory footprint, and computational and storage costs. In addition, we review available datasets for medical mask detection and train YOLOv5 on the Properly Wearing Masked Faces Dataset (PWMFD). Next, we test and evaluate a set of specific optimization techniques, transfer learning, data augmentations, and attention mechanisms, and we report on their effect for real-time mask detection. Based on our findings, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD while maintaining the same level of mean Average Precision (67%). Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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16 pages, 2991 KiB

Open AccessArticle

Technology Tools in Hospitality: Mapping the Landscape through Bibliometric Analysis and Presentation of a New Software Solution

by Thomas Krabokoukis

Digital 2023, 3(1), 81-96; https://doi.org/10.3390/digital3010006 - 3 Mar 2023

Viewed by 3687

Abstract

This study offers a comprehensive examination of the literature surrounding technology and tools in the hospitality industry. A bibliometric analysis was performed on 709 Scopus-indexed publications from 2000 to January 2023, with a focus on identifying key players, institutions, research trends, and the co-occurrence of keywords. The results shed light on the scientific landscape of technology and tools in the hospitality sector, emphasizing the significance of big data and the customer experience in the sharing economy. The study also presents the architecture of new software that offers guests the ability to customize their hotel stay, classified as part of the first cluster in the co-occurrence of keywords analysis. This approach highlights the growing importance of big data and customer experience and makes a valuable contribution to the field by offering a tool for hotel booking customization. Furthermore, the study underscores the importance of collaboration between academic institutions and private companies in providing a mutually beneficial platform that exceeds the expectations of both hotels and guests. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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18 pages, 789 KiB

Open AccessArticle

Decision-Making Approach for an IoRT-Aware Business Process Outsourcing

by Najla Fattouch, Imen Ben Lahmar, Mouna Rekik and Khouloud Boukadi

Digital 2022, 2(4), 520-537; https://doi.org/10.3390/digital2040028 - 4 Nov 2022

Cited by 3 | Viewed by 2521

Abstract

In the context of Industry 4.0, IoRT-aware BPs represent an attractive paradigm that aims to automate the classic business process (BP) using the internet of robotics things (IoRT). Nonetheless, the execution of these processes within the enterprises may be costly due to the consumed resources, recruitment cost, etc. To bridge these gaps, the business process outsourcing (BPO) strategy can be applied to outsource partially or totally a process to external service suppliers. Despite the various advantages of BPO, it is not a trivial task for enterprises to determine which part of the process should be outsourced and which environment would be selected to deploy it. This paper deals with the decision-making outsourcing of an IoRT-aware BP to the fog and/or cloud environments. The fog environment includes devices at the edge of the network which will ensure the latency requirements of some latency-sensitive applications. However, relying on cloud, the availability and computational requirements of applications can be met. Toward these objectives, we realized an in-depth analysis of the enterprise requirements, where we identified a set of relevant criteria that may impact the outsourcing decision. Then, we applied the method based on the removal effects of criteria (MEREC) to automatically generate the weights of the identified criteria. Using these weights, we performed the selection of the suitable execution environment by using the ELECTRE IS method. As an approach evaluation, we sought help from an expert to estimate the precision, recall, and F-score of our approach. The obtained results show that our approach is the most similar to the expert result, and it has acceptable values. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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19 pages, 4033 KiB

Open AccessArticle

Significance of Machine Learning for Detection of Malicious Websites on an Unbalanced Dataset

by Ietezaz Ul Hassan, Raja Hashim Ali, Zain Ul Abideen, Talha Ali Khan and Rand Kouatly

Digital 2022, 2(4), 501-519; https://doi.org/10.3390/digital2040027 - 31 Oct 2022

Cited by 41 | Viewed by 5036

Abstract

It is hard to trust any data entry on online websites as some websites may be malicious, and gather data for illegal or unintended use. For example, bank login and credit card information can be misused for financial theft. To make users aware of the digital safety of websites, we have tried to identify and learn the pattern on a dataset consisting of features of malicious and benign websites. We treated the problem of differentiation between malicious and benign websites as a classification problem and applied several machine learning techniques, for example, random forest, decision tree, logistic regression, and support vector machines to this data. Several evaluation metrics such as accuracy, precision, recall, F1 score, and false positive rate, were used to evaluate the performance of each classification technique. Since the dataset was imbalanced, the machine learning models developed a bias during training toward a specific class of websites. Multiple data balancing techniques, for example, undersampling, oversampling, and SMOTE, were applied for balancing the dataset and removing the bias. Our experiments showed that after balancing the data, the random forest algorithm using the oversampling technique showed the best results in all evaluation metrics for the benign and malicious website feature dataset. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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19 pages, 350 KiB

Open AccessArticle

Explicit and Implicit Trust Modeling for Recommendation

by Utku Demirci and Pinar Karagoz

Digital 2022, 2(4), 444-462; https://doi.org/10.3390/digital2040024 - 29 Sep 2022

Cited by 1 | Viewed by 2622

Abstract

Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene as an important concept to improve the recommendation performance. Trust describes the nature and the strength of ties between individuals and hence provides useful information to improve the recommendation accuracy, particularly against data sparsity and cold start problems. The Trust notion helps alleviate the effect of these problems by providing additional reliable relationships between the users. However, trust information, specifically explicit trust, is not straightforward to collect and is only scarcely available. Therefore, implicit trust models have been proposed to fill in the gap. The literature includes a variety of studies proposing the use of trust for recommendation. In this work, two specific sub-problems are elaborated on: the relationship between explicit and implicit trust scores, and the construction of a machine learning model for explicit trust. For the first sub-problem, an implicit trust model is devised and the compatibility of implicit trust scores with explicit scores is analyzed. For the second sub-problem, two different explicit trust models are proposed: Explicit trust modeling through users’ rating behavior and explicit trust modeling as a link prediction problem. The performances of the prediction models are analyzed on a set of benchmark data sets. It is observed that explicit and implicit trust models have different natures, and are to be used in a complementary way for recommendation. Another important result is that the accuracy of the machine learning models for explicit trust is promising and depends on the availability of data. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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20 pages, 4016 KiB

Open AccessArticle

Data-Driven Decision Support for Adult Autism Diagnosis Using Machine Learning

by Sotirios Batsakis, Marios Adamou, Ilias Tachmazidis, Sarah Jones, Sofya Titarenko, Grigoris Antoniou and Thanasis Kehagias

Digital 2022, 2(2), 224-243; https://doi.org/10.3390/digital2020014 - 11 May 2022

Cited by 5 | Viewed by 3530

Abstract

Adult referrals to specialist autism spectrum disorder diagnostic services have increased in recent years, placing strain on existing services and illustrating the need for the development of a reliable screening tool, in order to identify and prioritize patients most likely to receive an ASD diagnosis. In this work a detailed overview of existing approaches is presented and a data driven analysis using machine learning is applied on a dataset of adult autism cases consisting of 192 cases. Our results show initial promise, achieving total positive rate (i.e., correctly classified instances to all instances ratio) up to 88.5%, but also point to limitations of currently available data, opening up avenues for further research. The main direction of this research is the development of a novel autism screening tool for adults (ASTA) also introduced in this work and preliminary results indicate the ASTA is suitable for use as a screening tool for adult populations in clinical settings. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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21 pages, 379 KiB

Open AccessArticle

Intelligence-Led Policing and the New Technologies Adopted by the Hellenic Police

by Georgios Gkougkoudis, Dimitrios Pissanidis and Konstantinos Demertzis

Digital 2022, 2(2), 143-163; https://doi.org/10.3390/digital2020009 - 29 Mar 2022

Cited by 2 | Viewed by 12031

Abstract

In the never-ending search by Law Enforcement Agencies (LEAs) for ways to reduce crime more effectively, the prevention of criminal activity is always considered the ideal solution. Since the 1990s, Intelligence-led Policing (ILP) was implemented in some forms by many LEAs around the world for crime prevention. Along with ILP, LEAs nowadays more and more turn to various new surveillance technologies. As a result, there are numerous studies and reports introducing some compelling results from LEAs that have implemented ILP, offering robust data around how the future of policing could be. In this context, this paper explores the most recent literature, identifying where ILP stands today in Greece and to what extent it could be a viable, practical approach to crime prevention. In addition, it is researched to what degree new technologies have been adopted by the European Union and the Hellenic Police in their “battle” against crime. It is concluded that most technologies are at the research stage, and studies are underway in many areas. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

23 pages, 1004 KiB

Open AccessArticle

A Regulatory Readiness Assessment Framework for Blockchain Adoption in Healthcare

by Olanrewaju Sanda, Michalis Pavlidis and Nikolaos Polatidis

Digital 2022, 2(1), 65-87; https://doi.org/10.3390/digital2010005 - 11 Mar 2022

Cited by 6 | Viewed by 4893

Abstract

Blockchain is now utilized by a diverse spectrum of applications and is proclaimed as a technological innovation that transforms the way that data are stored. This technology has the potential to transform the healthcare sector, especially the prevalent issues of patient’s data-privacy and fragmented healthcare data. However, there is no evidence-based effort to develop a readiness assessment framework for blockchain that combines all the different social and economic factors and involves all stakeholders. Based on a systematic literature review, the proposed framework is applied to Portugal’s healthcare sector and its applicability is outlined. The findings in this paper show the unique importance of regulators and the government in achieving a globally acceptable regulatory framework for the adoption of blockchain technology in healthcare and other sectors. The business entities and solution providers are ready to leverage the opportunities of blockchain, but the absence of a widely acceptable regulatory framework that protect stakeholders’ interests is slowing down the adoption of blockchain. There are several misconceptions regarding blockchain laws and regulations, which has slowed stakeholder readiness. This paper will be useful as a guideline and knowledge base to reinforce blockchain adoption. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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7 pages, 233 KiB

Open AccessCommunication

Use of Internet Technology among Older Adults in Residential Aged Care Facilities: Protocol for a Systematic Review and Meta-Analysis

by Sandesh Pantha, Sumina Shrestha and Janette Collier

Digital 2022, 2(1), 46-52; https://doi.org/10.3390/digital2010003 - 25 Feb 2022

Viewed by 3286

Abstract

Internet usage may help promote the physical and mental health of older adults living in Residential Aged Care Facilities (RACF). There is little evidence of how these older citizens use internet services. This systematic review aims to explore the trends and factors contributing to internet use among aged care residents. A systematic search will be conducted on nine online databases—MEDLINE, EMBASE, PsycInfo, CINAHL, AgeLine, ProQuest, Web of Science, Scopus, and the Cochrane Library. Two reviewers will independently conduct title and abstract screening, full-text reading, critical appraisal, and data extraction. Any discrepancies will be resolved by consensus. Methodological risk of bias will be assessed using the Effective Public Health Practice Project measure and Joanna Briggs Institute checklist. We will report a narrative synthesis of the evidence. Information on factors contributing to internet use and their strength of association will be reported. If feasible, we will undertake a meta-analysis and meta-synthesis. Our review will provide information on the factors predicting internet use among older adults in residential aged care facilities. The evidence from this review will help to formulate further research objectives and, potentially, to design an intervention to trial internet access for these groups. (Protocol Registration: PROSPERO-CRD 42020161227). Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

Review

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26 pages, 3230 KiB

Open AccessReview

Unlocking the Power of Digital Commons: Data Cooperatives as a Pathway for Data Sovereign, Innovative and Equitable Digital Communities

by Michael Max Bühler, Igor Calzada, Isabel Cane, Thorsten Jelinek, Astha Kapoor, Morshed Mannan, Sameer Mehta, Vijay Mookerje, Konrad Nübel, Alex Pentland, Trebor Scholz, Divya Siddarth, Julian Tait, Bapu Vaitla and Jianguo Zhu

Digital 2023, 3(3), 146-171; https://doi.org/10.3390/digital3030011 - 29 Jun 2023

Cited by 23 | Viewed by 6970

Abstract

Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship, new skills, and jobs, especially in small communities and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being, and protect digital rights, we propose data cooperatives as a vehicle for secure, trusted, and sovereign data exchange. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted application programming interfaces (“APIs”) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This review article analyses an array of transformative use cases that underline the potential of cooperative data governance. These case studies exemplify how data and platform cooperatives, through their innovative value creation mechanisms, can elevate digital commons and value chains to a new dimension of collaboration, thereby addressing pressing societal issues. Guided by our research aim, we propose a policy framework that supports the practical implementation of digital federation platforms and data cooperatives. This policy blueprint intends to facilitate sustainable development in both the Global South and North, fostering equitable and inclusive data governance strategies. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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Other

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10 pages, 749 KiB

Open AccessOpinion

Mouse Tracking as a Method for Examining the Perception and Cognition of Digital Maps

by Vassilios Krassanakis and Loukas-Moysis Misthos

Digital 2023, 3(2), 127-136; https://doi.org/10.3390/digital3020009 - 30 May 2023

Cited by 2 | Viewed by 2984

Abstract

This article aims to present the authors’ perspective regarding the challenges and opportunities of mouse-tracking methodology while performing experimental research, particularly related to the map-reading process. We briefly describe existing metrics, visualization techniques and software tools utilized for the qualitative and quantitative analysis of experimental mouse-movement data towards the examination of both perceptual and cognitive issues. Moreover, we concisely report indicative examples of mouse-tracking studies in the field of cartography. The article concludes with summarizing mouse-tracking strengths/potential and limitations, compared to eye tracking. In a nutshell, mouse tracking is a straightforward method, particularly suitable for tracking real-life behaviors in interactive maps, providing the valuable opportunity for remote experimentation; even though it is not suitable for tracking the actual free-viewing behavior, it can be concurrently utilized with other state-of-the-art experimental methods. Full article

(This article belongs to the Special Issue “Management of Digital Ecosystems”: Dedicated to the Memory of Prof. William I. Grosky 8/4/1944–11/13/2020)

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