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  FORMATION OF THE POST-PANDEMIC BUSINESS ENVIRONMENT IN GEORGIA: CHALLENGES AND PREDICTIONS

  The COVID 19 pandemic has transformed the entire world's strategy. The governmental oversight, fundamental and significant views were renewed and prioritized according to the unpredictable pandemic process and force-majeure demand. The world's governments and private sectors have faced new socio-political and economic problems and challenges that ... The COVID 19 pandemic has transformed the entire world's strategy. The governmental oversight, fundamental and significant views were renewed and prioritized according to the unpredictable pandemic process and force-majeure demand. The world's governments and private sectors have faced new socio-political and economic problems and challenges that require quick, effective and powerful solutions. The nation's economic viability during the pandemic and post-pandemic periods depended on these decisions and their effectiveness. Monetary, fiscal, and financial policies suffered significant difficulties in maintaining the economic system. Covid 19 has created difficulties in predicting economic sequences alongside social problems and the complete lack of economic precision in all segments has caused macroeconomic uncertainty. This paper deals with the microeconomic analysis of Georgia in the post-pandemic period, discussing both the main economic parameters of the country and its dynamics, as well as the analysis of its impact on the microeconomic situation. The paper analyzes the impact of the pandemic on the main sectors, as well as the decisions made by the state for economic stimulation and its effect. The work is mainly based on the official information of state structures and international organizations, as well as the works of Georgian and foreign scientists, and based on them, a coherent, multi-component analysis is presented. The theoretical framework of the article is based on fundamental, conceptual studies of Georgian and foreign authors in the field of our research. The paper presents conclusions and recommendations based on a complex study of macroeconomic factors, which are important for scientific circles, both the state and the private sector.

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  ADVANTAGES AND ETHICAL CONSIDERATIONS OF INDUSTRIAL IOT ARTIFICIAL INTELLIGENCE SOLUTIONS USAGE

  The objective of this paper is to research the synergistic interaction between Industrial Internet of Things (IIoT) and the application of Artificial Intelligence (AI) in IIoT ecosystems (AIoT). For achieving this purpose, a method of integrative study of leading scientific publications on the topics ‘Industrial Internet of Things’ and ‘Artificial ... The objective of this paper is to research the synergistic interaction between Industrial Internet of Things (IIoT) and the application of Artificial Intelligence (AI) in IIoT ecosystems (AIoT). For achieving this purpose, a method of integrative study of leading scientific publications on the topics ‘Industrial Internet of Things’ and ‘Artificial Internet of Things’ and the PRIZMA flow chart for filtering the list of found documents are applied. The inability of full coverage of the AIoT topic in one article has necessitated the usage of an approach for its content analysis in several thematic groups. The IIoT and AIoT concepts has been explored by various theoretical and practical aspects in many scientific papers published mainly over the past five years, but continues to arouse the interest of researchers. The novelty of the manuscript could be viewed in the attempts of systematizing the key characteristics, advantages, and risks in the practical use of these technologies in the form of smart manufacturing machines, robotic devices, autonomous vehicles, and software for predictive maintenance, and in outlining the possibility for green and sustainable AIoT platforms creation. It is concluded that a key requirement for the implementation of AIoT solutions is the consideration of the various threats and ethical aspects of their design, which contributes to the correct definition of the ‘social value’ of such business initiatives.

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  METADATA MANAGEMENT FRAMEWORK FOR BUSINESS INTELLIGENCE DRIVEN DATA LAKES

  Data lakes (DL) provide powerful capabilities for processing and utilizing large and diverse data, helping organizations adapt to the modern environment and extract maximum value from the information at their disposal. Effective data analysis provides actionable knowledge which is a competitive advantage for organizations. Metadata management in ... Data lakes (DL) provide powerful capabilities for processing and utilizing large and diverse data, helping organizations adapt to the modern environment and extract maximum value from the information at their disposal. Effective data analysis provides actionable knowledge which is a competitive advantage for organizations. Metadata management in data lakes is a key element in ensuring their full functionality. At the same time, this is a dynamic and under-researched area that reflects the rapid development of information technology and the business needs for effective data management. The research is based on a thorough scientific analysis of existing publications on the chosen topic. For this purpose, up-to-date and relevant open access publications from Scopus and Web of Science that correspond to the keywords "data lake" and "metadata" are identified and are from the last 15 years. Based on a review of the existing literature, the main challenges in data lake metadata management are highlighted. The goal of the research is to summarize the existing models in the field of metadata management in data lakes and to propose a new conceptual framework that can serve as a useful guide for designing and implementing metadata management models in heterogeneous data warehouses, as well as implementation steps. The concept's adoption involves a detailed study of the data management model in a specific organization, a measurement of the level of effectiveness after the model’s implementation, and the use of additional metrics to confirm its feasibility. These tasks are therefore the subject of future research. Another limitation of the proposed framework is that it does not address in depth the rules and standards related to ensuring data security, which would be of the highest priority especially in sectors such as finance, defence and healthcare. In addition, further research could also focus on future analysis of the level of satisfaction with the transformation of metadata management processes.