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Information
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Data-Driven Decision-Making in Intelligent Systems
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Data-Driven Decision-Making in Intelligent Systems

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 897

Special Issue Editor

Dr. Alexandros Bousdekis

E-Mail Website
Guest Editor
Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Greece
Interests: Industry 4.0; intelligent systems; management of information systems; predictive and prescriptive analytics; real-time decision-making; proactive and event-driven computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data science encompasses the principles, processes, and methods used to analyze data in order to derive useful insights. In this way, data analytics is capable of enhancing decision-making in the business context, either referring to long-term management decisions or to (near) real-time decisions, usually at the operational level. The literature, but also real-life applications, demonstrate that data-driven decision-making exhibits higher productivity and efficiency, as well as higher returns on assets, equity, and market value. Therefore, there is a wide range of actual and potential applications, such as direct marketing, online advertising, credit scoring, financial trading, fraud detection, search ranking, telecommunications, product recommendations, manufacturing operations, etc. With the emergence of intelligent systems, more and more information systems have been developed in order to automate data-driven decision-making. However, information systems development has to face new challenges derived from the large amounts of data, from heterogeneous data sources, existing in the business environments. This Special Issue welcomes research works dealing with the design and development of intelligent systems for data-driven decision-making. In this sense, it welcomes research works on architectures, algorithms, methods, and application-oriented research on exploiting data for supporting decision-making with the use of information systems.

Dr. Alexandros Bousdekis
Guest Editor

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 1800 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

  • intelligent systems
  • data analytics
  • machine learning
  • decision-making
  • information systems for data-driven decision-making

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Published Papers (2 papers)

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16 pages, 307 KiB  
Article
Being (Not) Successful in Internationalisation After Receiving Export Support: Which Predictors Are Able to Forecast It and How Accurately?
by Oliver Lukason and Tiia Vissak
Information 2025, 16(7), 544; https://doi.org/10.3390/info16070544 - 27 Jun 2025
Viewed by 157
Abstract
This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated [...] Read more.
This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated predictors grouped into four domains are used to forecast 24 different proxies of (non-)success with logistic regression and neural networks. The domains focus on firms’ general characteristics, earlier financial and export performance, and export-grant-specific characteristics. The highest areas under the curve exceed the 0.9 threshold, therefore indicating excellent predictive abilities, while more specific (non-)success proxies can be predicted less accurately than general ones. Predictors portraying firm size and export support size emerge as the best in the case of both methods, while in different neural networks, at least one predictor from each of the four domains is among the most important ones. These results lead to multiple practical implications concerning how to select firms into export grant programmes. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)
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54 pages, 625 KiB  
Systematic Review
The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review
by Tim Nolte and Sven Tomforde
Information 2025, 16(6), 496; https://doi.org/10.3390/info16060496 - 14 Jun 2025
Viewed by 254
Abstract
Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview [...] Read more.
Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview of CM techniques, application areas, and input data. It also assesses the extent to which current approaches support self-* properties, real-time operation, and predictive functionality. Out of 284 retrieved publications, 110 were selected for detailed analysis. About 38.71% focus on manufacturing, 65.45% on system-level monitoring, and 6.36% on static structures. Most approaches (69.09%) use Machine Learning (ML), while only 18.42% apply Deep Learning (DL). Predictive techniques are used in 16.63% of the studies, with 38.89% combining prediction and anomaly detection. Although 58.18% implement some self-* features, only 42.19% present explicitly self-adaptive or self-organizing methods. A mere 6.25% incorporate feedback mechanisms. No study fully combines self-adaptation and self-organization. Only 5.45% report processing times; however, 1000 Hz can be considered a reasonable threshold for high-frequency, real-time CM. These results highlight a significant research gap and the need for integrated SASO capabilities in future CM systems—especially in real-time, autonomous contexts. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)
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