Topic Editors

School of Business, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi, Greece
Faculty of Theoretical and Applied Economics, The Bucharest University of Economic Studies, Romana Square, No. 6, 010374 Bucharest, Romania

Big Data and Artificial Intelligence, 2nd Volume

Abstract submission deadline
closed (31 January 2024)
Manuscript submission deadline
31 March 2024
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Topic Information

Dear Colleagues,

The evolution of research in Big Data and artificial intelligence in recent years challenges almost all domains of human activity. The potential of artificial intelligence to act as a catalyst for all given business models, and the capacity of Big Data research to provide sophisticated data and services ecosystems at a global scale, provide a challenging context for scientific contributions and applied research. This Topic section promotes scientific dialogue for the added value of novel methodological approaches and research in the specified areas. Our interest is on the entire end-to-end spectrum of Big Data and artificial intelligence research, from social sciences to computer science including, strategic frameworks, models, and best practices, to sophisticated research related to radical innovation. The topics include, but are not limited to, the following indicative list:

  • Enabling Technologies for Big Data and AI research:
    • Data warehouses;
    • Business intelligence;
    • Machine learning;
    • Neural networks;
    • Natural language processing;
    • Image processing;
    • Bot technology;
    • AI agents;
    • Analytics and dashboards;
    • Distributed computing;
    • Edge computing.
  • Methodologies, frameworks, and models for artificial intelligence and Big Data research:
    • Towards sustainable development goals;
    • As responses to social problems and challenges;
    • For innovations in business, research, academia industry, and technology;
    • For theoretical foundations and contributions to the bodyf knowledgef AI and Big Data research.
  • Best practices and use cases;
  • Outcomesf R&D projects;
  • Advanced data science analytics;
  • Industry-government collaboration;
  • Systemsf information systems;
  • Interoperability issues;
  • Security and privacy issues;
  • Ethicsn Big Data and AI;
  • Social impactf AI;
  • Open data.

Prof. Dr. Miltiadis D. Lytras
Prof. Dr. Andreea Claudia Serban
Topic Editors



  • artificial intelligence
  • big data
  • machine learning
  • open data
  • decision making

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Big Data and Cognitive Computing
3.7 4.9 2017 18.2 Days CHF 1800 Submit
2.6 3.2 2013 21.4 Days CHF 1800 Submit
3.1 5.8 2010 18 Days CHF 1600 Submit
Remote Sensing
5.0 7.9 2009 23 Days CHF 2700 Submit
3.9 5.8 2009 18.8 Days CHF 2400 Submit is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

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Published Papers (1 paper)

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23 pages, 4267 KiB  
Nowcasting Unemployment Using Neural Networks and Multi-Dimensional Google Trends Data
Economies 2023, 11(5), 130; - 25 Apr 2023
Viewed by 2338
This article forms an attempt to expand the ability of online search queries to predict initial jobless claims in the United States and further explore the intricacies of Google Trends. In contrast to researchers who used only a small number of search queries [...] Read more.
This article forms an attempt to expand the ability of online search queries to predict initial jobless claims in the United States and further explore the intricacies of Google Trends. In contrast to researchers who used only a small number of search queries or limited themselves to job agency explorations, we incorporated keywords from the following six dimensions of Google Trends searches: job search, benefits, and application; mental health; violence and abuse; leisure search; consumption and lifestyle; and disasters. We also propose the use of keyword optimization, dimension reduction techniques, and long-short memory neural networks to predict future initial claims changes. The findings suggest that including Google Trends keywords from other dimensions than job search leads to the improved forecasting of errors; however, the relationship between jobless claims and specific Google keywords is unstable in relation to time. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)
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