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Recent Advances in Big Data-Driven Prescriptive Analytics
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Recent Advances in Big Data-Driven Prescriptive Analytics

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 1914

Special Issue Editors

Prof. Dr. Sadok Ben Yahia

E-Mail Website
Guest Editor
Centre for Industrial Software, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 6400 Sønderborg, Denmark
Interests: big data; intelligent transport systems; XAI and Generative AI; ontology engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Amnir Hadachi

E-Mail Website
Guest Editor
Intelligent Transportation Systems Lab, Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
Interests: intelligent transportation systems; applied machine learning; computer vision; mobility modeling; data mining
Prof. Dr. Jenq-Shiou Leu

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan
Interests: mobile service and platform design; application development of computational intelligence; distributed computing (edge/cloud computing)

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue entitled “Recent Advances in Big Data-Driven Prescriptive Analytics”, which is part of the MDPI journal Big Data and Cognitive Computing.

Traditionally, descriptive analytics have been strongly associated with passive data collection activities like reporting, performance monitoring, and dashboards. In recent years, business and social practices have digitized with the evolution of new enabling technologies such as artificial intelligence (AI), machine learning (ML), deep learning (DL), distributed ledger technology (DLT), and 6G, to name but a few. These supporting technologies, grounding the concept of prescriptive analytics, strive for relentless efficiency and business agility to address new revenue opportunities and to meet or exceed customer expectations through a superior experience.

The ultimate goal is to attain a new level of automation and intelligence to achieve automated end-to-end services, better known as zero-touch systems, efficiently and seamlessly, that is, to provide an adequate ecosystem of services, infrastructure, and capabilities to achieve fully integrated Self-X life cycle operations (self-serving, self-fulfilling, and self-assuring).

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Collection and analysis of big data;
  • Advanced AI for prescriptive systems (explainable AI, generative AI, etc.);
  • Digital ethics for prescriptive systems;
  • Big data fusion technology;
  • Machine learning technologies and applications (federated learning, active learning, etc.);
  • Deep learning technologies and applications;
  • Zero-touch systems and architectures;
  • Distributed computing for prescriptive systems;
  • Intelligent transport prescriptive systems;
  • Intrusion detection for prescriptive systems;
  • Edge computing for prescriptive systems;
  • Large language models for prescriptive models;
  • Futuristic paradigms for advanced use cases: adopting blockchain, quantum computing, etc.

Prof. Dr. Sadok Ben Yahia
Dr. Amnir Hadachi
Prof. Dr. Jenq-Shiou Leu
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. Big Data and Cognitive Computing 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

  • big data
  • data-driven approaches
  • advanced AI techniques
  • prescriptive approaches

Published Papers (2 papers)

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Research

18 pages, 577 KiB  
Article
Application of Natural Language Processing and Genetic Algorithm to Fine-Tune Hyperparameters of Classifiers for Economic Activities Analysis
by Ivan Malashin, Igor Masich, Vadim Tynchenko, Vladimir Nelyub, Aleksei Borodulin and Andrei Gantimurov
Big Data Cogn. Comput. 2024, 8(6), 68; https://doi.org/10.3390/bdcc8060068 - 13 Jun 2024
Viewed by 532
Abstract
This study proposes a method for classifying economic activity descriptors to match Nomenclature of Economic Activities (NACE) codes, employing a blend of machine learning techniques and expert evaluation. By leveraging natural language processing (NLP) methods to vectorize activity descriptors and utilizing genetic algorithm [...] Read more.
This study proposes a method for classifying economic activity descriptors to match Nomenclature of Economic Activities (NACE) codes, employing a blend of machine learning techniques and expert evaluation. By leveraging natural language processing (NLP) methods to vectorize activity descriptors and utilizing genetic algorithm (GA) optimization to fine-tune hyperparameters in multi-class classifiers like Naive Bayes, Decision Trees, Random Forests, and Multilayer Perceptrons, our aim is to boost the accuracy and reliability of an economic classification system. This system faces challenges due to the absence of precise target labels in the dataset. Hence, it is essential to initially check the accuracy of utilized methods based on expert evaluations using a small dataset before generalizing to a larger one. Full article
(This article belongs to the Special Issue Recent Advances in Big Data-Driven Prescriptive Analytics)
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23 pages, 1647 KiB  
Article
Harnessing Graph Neural Networks to Predict International Trade Flows
by Bassem Sellami, Chahinez Ounoughi, Tarmo Kalvet, Marek Tiits and Diego Rincon-Yanez
Big Data Cogn. Comput. 2024, 8(6), 65; https://doi.org/10.3390/bdcc8060065 - 7 Jun 2024
Cited by 1 | Viewed by 818
Abstract
In the realm of international trade and economic development, the prediction of trade flows between countries is crucial for identifying export opportunities. Commonly used log-linear regression models are constrained due to difficulties when dealing with extensive, high-cardinality datasets, and the utilization of machine [...] Read more.
In the realm of international trade and economic development, the prediction of trade flows between countries is crucial for identifying export opportunities. Commonly used log-linear regression models are constrained due to difficulties when dealing with extensive, high-cardinality datasets, and the utilization of machine learning techniques in predictions offers new possibilities. We examine the predictive power of Graph Neural Networks (GNNs) in estimating the value of bilateral trade between countries. We work with detailed UN Comtrade data that represent annual bilateral trade in goods between any two countries in the world and more than 5000 product groups. We explore two different types of GNNs, namely Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), by applying them to trade flow data. This study evaluates the effectiveness of GNNs relative to traditional machine learning techniques such as random forest and examines the possible effects of data drift on their performance. Our findings reveal the superior predictive capability of GNNs, suggesting their effectiveness in modeling complex trade relationships. The research presented in this work offers a data-driven foundation for decision-making and is relevant for business strategies and policymaking as it helps in identifying markets, products, and sectors with significant development potential. Full article
(This article belongs to the Special Issue Recent Advances in Big Data-Driven Prescriptive Analytics)
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