Стратегічне забезпечення розвитку персоналу за умов цифрової трансформації та глобалізації

In the big data processing systems, the amount of data is increasing. At the same time, the real-time requirement of data processing and analysis is higher and higher. Therefore, it is required that the big data processing and analysis systems have better performance. Job scheduling plays an important role in improving the overall system performance in big data processing frameworks. However, job scheduling is a difficult NP-hard problem. There are many factors that need to be considered for job scheduling. For example, jobs have dependencies among stages, therefore we should not allocate resources to tasks that are not ready. Sometimes, there are constraints between jobs. These are a challenge to the scheduling performance of big data processing and analysis systems. In this paper, we try to solve the problem by translating it into Boolean Satisfiability Problem (SAT) which is an exact method. SAT-based scheduling algorithm is not a new approach, but in the past it mainly used to solve the static scheduling problems. For dynamic scheduling system, it requires all problems to be solved within a limited time, which is a challenge for SAT encoding. In this paper, we refer to the previous SAT solution to the Job Shop Scheduling Problem, and adjust the algorithm to meet the requirements of the big data processing system. At the same time, we optimized the coding approach and reduced the number of clauses. Thus, the efficiency of the problem solved is improved to meet the performance requirements. The experimental results show that the number of clauses is reduced by more than 30%, and the processing time of the SAT solver to get the solution can be reduced by more than 50%. To demonstrate its effectiveness, we have also implemented our new job scheduler in Apache Hadoop YARN, and validated its effectiveness

The development of innovative information technologies in the modern world has become a necessity for improving the efficiency of activities of financial institutions and improving the quality of financial services provided to economic agents. The State plays an important role in creating a favorable environment for the implementation of such technologies. However, the introduction of innovative technologies by financial institutions is associated with risks that require the State-based regulation to ensure the stability and reliability of functioning of a financial system. The article discusses the issues of improving the State-based regulation of the processes of development and introduction of information and innovative technologies by financial institutions in Ukraine. Particular attention is paid to the substantiation of the need for such regulation. It is determined that the construction of a model of such regulation should be based on the balance of interests of financial institutions, their desire to implement digital technologies for their own development, on the one hand and, on the other hand, their clients, who want to be sure that their personalized information will not be made public to third parties thus leading to a loss of financial resources. The article fragmentarily analyzes the current trends in the development of banking and non-bank financial institutions, describes the transformation processes that have taken place in the model of the State-based regulation of financial institutions in Ukraine. As a result, taking into account the current objective processes of digitalization of both the activities of financial institutions and public authorities that regulate them, measures for the formation and ensuring the effectiveness of the State-based regulation on the introduction of information and communication technologies by financial institutions are proposed, namely: creation of a transparent and open regulatory environment; ensuring a high level of cybersecurity and protection of the customers’ personal data; promoting active cooperation between the State-based regulators, financial institutions, and public organizations for the development and implementation of innovative solutions in the field of balanced use of such technologies.

B a c k g r o u n d . Public administration is formed as a complex and dynamic system that reflects the evolution of social relations and the changing role of the state in development processes. Today, public administration increasingly moves beyond administrative control, transforming into a space of partnership, participation, and openness. The digitalization of administrative processes, the emergence of e-government, and the concept of open government have reshaped the nature of public authority. M e t h o d s . The study employs a set of general scientific and special methods that ensured a comprehensive analysis of the evolution of public administration. The historical-genetic method was used to identify the sequence of development of administrative models. The comparative method enabled the examination of classical and modern theories. The system analysis method made it possible to determine the relationship between the principles of public administration and digital transformation processes. Content analysis of academic publications and international documents allowed for the generalization of trends in contemporary governance and the identification of directions for its transformation. R e s u l t s . The essence of public administration is clarified as a multidimensional category that integrates normative-administrative, valueoriented, and communicative dimensions and reflects the changing role of the state in the context of digital transformation. The basic principles of public administration were systematized, and the features of their transformation under the influence of digital technologies were identified. The stages of development of public administration models were summarized. Contemporary paradigms such as Digital Era Governance, Public Value Management, and Collaborative Governance were analyzed. It was substantiated that digitalization functions as an integrative factor that connects organizational modernization, a value-based approach, and network interaction, creating preconditions for the transition to a digital-social model of public administration. C o n c l u s i o n s . The study demonstrates that the evolution of public administration is moving from administrative-hierarchical systems toward integrated, open, and value-oriented models of governance. Digitalization not only modernizes tools and procedures but also reshapes governance orientations, shifting the focus toward transparency, participation, and the creation of public value. The results may serve as a methodological basis for further research on digital governance.

The introduction of innovative technologies is essential for ensuring information security in Kazakhstan. This research aimed to determine the main success factors for applying innovative technologies to guarantee reliable information protection in Kazakhstan. The methodological approach combined system analysis of Kazakhstan's information security legislation with empirical modeling of key principles for developing and implementing technological innovations. The results showed that Kazakhstan's government supports technological innovations in information security through regulations and programs like "Digital Kazakhstan." However, challenges exist due to underdeveloped digital infrastructure. Threats to information security in Kazakhstan include multi-ethnic tensions, media dependence on commercial entities, a lack of a domestic electronics industry, and the erosion of cultural values. Effective innovations include integrated circuits, high-tech components, virtual client protection, enterprise network support, big data analytics, improved cloud security, and container security. Their benefits over conventional systems include better resource efficiency, scaled capacity, accelerated development, improved responsiveness, and innovation. Recommended innovations for Kazakhstan include protecting virtual clients, supporting corporate network deployment, leveraging big data, advancing container security, and boosting cloud technologies.

Construction is one of the most significant sectors of the economy both by the share of the population employed in the industry and in the share of gross value added. Simultaneously сonstruction is one of the outsiders of digital transformation processes, which are now a trend in the development of the Russian economy. However, the construction industry has significant potential and prerequisites for the introduction of digital technologies, including building information modeling (BIM). A relevant scientific and practical task is to identify and systematize the factors (groups of factors) that have a significant impact on the introduction of innovative technologies in construction. This article presents the results of such systematisation with a focus on the implementation of BIM. The object of study was the enterprises of the investment and construction sphere of various focus, carrying out the transition to innovative digital technologies, including BIM. The subject is the factors of internal and external in relation to the organization of the nature that influence on the processes under study. The result of the study is a structured system of these factors, including external and internal environmental factors that may or may not be manageable in a particular management situation.

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

The article discusses current issues of managing an organization's business processes in the digital economy, as well as their transformation under the influence of digital technologies. The purpose of the research is to study approaches to assessing the effectiveness of digital transformation, to determine its nature and essence at a theoretical level, as well as to conduct an econometric analysis of the contribution of digital changes to improving company efficiency. The research methodology includes deduction and induction in analyzing the activities of companies implementing digital transformation, as well as the use of analytical methods and regression analysis. In the course of the work, existing approaches to defining digital transformation and evaluating its effectiveness were studied. Based on a regression analysis of the largest companies of the pre-digital era, the hypothesis of a significant impact of digital transformation on short-term profitability was refuted, which allowed us to conclude about its strategic nature and long-term effects. The originality and value of the research lies in the development of the theoretical foundations for assessing the digital transformation of business, which contributes to the improvement of crisis management and determines the directions of further scientific research in this area. The results obtained allow us to take a fresh look at the processes of digital transformation and their impact on business efficiency.

The presented material identifies the main areas of improvement of the accounting and control system, in particular, the introduction of innovative technologies, strengthening internal control, improving the quality of financial reporting and harmonizing the accounting methodology in accordance with international standards. One of the key components of improving the accounting and control system is the use of modern information technologies, such as ERP systems, artificial intelligence, big data (Big Data) and blockchain. ERP systems ensure the integration of all functional units of the enterprise into a single information platform, which allows you to quickly receive analytical information for making management decisions. The introduction of blockchain technologies into the accounting and control system allows you to increase the transparency of financial transactions and reduce the risks of manipulation of accounting data. Due to the decentralized nature of the blockchain, the immutability of accounting records is ensured, which significantly complicates the possibility of falsifying financial documentation. The introduction of outsourcing of accounting functions is justified as one of the directions of improving the accounting and control system, which allows enterprises to focus on their core activities, transferring routine accounting and control processes to specialized organizations. The use of outsourcing makes it possible to reduce accounting costs, improve the quality of financial reporting and minimize risks associated with noncompliance with regulatory requirements. The author proposes the introduction of a risk-oriented approach, which allows identifying and minimizing potential threats to the financial stability of the enterprise. The use of risk management methods, such as COSO ERM, allows assessing the level of threats and developing measures to eliminate them in a timely manner. Monitoring of financial flows and liquidity management is of particular importance, which helps to ensure the stable operation of the enterprise even in crisis conditions. Improving accounting policies also involves increasing the role of cost management through the use of strategic control methods. Tools such as Activity-Based Costing (ABC) and Target Costing allow for more accurate determination of product costs and opportunities for their reduction.

Simple SummaryThe primary purpose of this review is to provide an in-depth analysis of existing Artificial Intelligence (AI) algorithms used in the field of prostate cancer (PC) for diagnosis and treatment. This review aims to show the research community that AI-enabled technologies have the potential for widespread growth and penetration of PC diagnostics and therapeutics to simplify and accelerate existing healthcare processes.As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Big Data (BD) is the massive amount of data that has been collected as a result of recent developments in sensor networks and IoT technology. More effective techniques with high analytical accuracy are required for the investigation of such vast amounts of data. The ability to analyze large amounts of data in real time is severely limited by the standard neural network and artificial intelligence algorithms. In the past several years, DL has started to take center stage in BD's analytics solutions. When it comes to BD analytics, DL can produce results that are more accurate, quicker, and scalable. In domains including natural language processing, speech recognition, and computer vision, it has achieved before unseen success. DL is an interesting and useful technique for BD analytics because of its capacity to extract high-level complicated representations as well as data scenarios, particularly unsupervised data from big volume data. To the best of our knowledge, no comprehensive survey covering all DL approaches for BD analytics exists, despite this interest. The current survey's goal is to examine the BD analytics research that has been done with DL methods. Several studies that offer very accurate analytical findings explore the potential use of DL with BD analytics.

Inactivity has been the main cause of obesity which has affected many people worldwide.Studies show that fitness is an important goal for a healthy lifestyle and has been used as a measurement for health-related quality of life.A fitness trainer can motivate and teach users to do fitness activities.However, to use a human fitness trainer may involve high cost and is not suitable for a certain school setting.A personalized virtual fitness trainer has the potential to replace a human trainer whenever possible.It can help physical education teachers in schools to motivate students to exercise.This study discussed the design and development of an AI-based application coined as an Intelligent Virtual Fitness Trainer (IVFIT) as an attempt to provide a personalized physical learning experience for school students.This app is developed to (1) demonstrate five workout activities for 30 minutes; (2) teach the user three types of dances for 22 minutes, and (3) demonstrate five basic self-defense moves.These training scenarios were administered to a physical education class consisting of 23 students in which the recommendation of the fitness trainer is done using a calculation of artificial intelligence algorithms.The percentage of prediction error was calculated after each activity to measure the effectiveness of the app in giving recommendations.Subsequently, a teacher observation test was conducted to determine students' perceptions of the effectiveness of the proposed VFT application.Overall, students found the VFT application to be effective in stimulating higher levels of involvement in physical activity without teacher supervision.It can be concluded from the study that a personalized virtual trainer can assist in assigning a suitable virtual trainer for specific users and can be used for physical education activities.

The article discloses innovative technologies in the activities of construction enterprises and increasing the efficiency of their implementation in the conditions of competition and dynamic changes. It has been established that the development and functioning of construction enterprises is under the influence of the innovation mechanism and applied innovations. Accordingly, in order to achieve success and ensure sustainable development, construction enterprises must implement innovative technologies that not only increase the efficiency of production processes, but also contribute to the creation of competitive advantages. It has been proven that the introduction of innovative technologies allows to reduce costs, reduce project execution time, improve the quality of construction works and improve labor safety. At the same time, the success of innovation implementation largely depends on the ability of enterprises to adapt to new conditions, learn new work methods and effectively manage changes. An analysis of the Index of construction products for the period of 2022 was carried out in terms of months and building types. The specified index reflects the change in the volumes of manufactured construction products (completed construction works) for the periods selected for comparison, deflated by the relevant price indices in construction. It was determined that a change in the index of construction products can have significant consequences for the economy and the social sphere, therefore it is important to carefully monitor this indicator and analyze the reasons for its changes in order to take timely measures to stabilize the situation. The results of the change of the Index of construction products are substantiated. The growth of the index of construction products means an increase in the volume of production in the construction industry for a certain period of time in comparison with the previous period. The growth of the Construction Products Index is an important indicator that indicates positive trends in the economy and the construction industry in particular. This creates optimistic prospects for further development and investment in the sector. It has been established that construction enterprises should pay special attention to the analysis of best practices and recommendations for increasing the efficiency of innovative processes, which in the context of constant changes in market conditions and increased competition will contribute to their economic growth.

Purpose. The aim of the article is to determine the place of cryptocurrencies in the digital economy, assess their state and development trends, research prospective directions for expanding the scope of their use by introducing innovative technologies and tools, and reducing the risks of investing in crypto assets. Methodology of research. The following research methods were used to achieve the set goal: induction and deduction – in the process of identifying cause-and-effect relationships and trends in the development of cryptocurrencies in the digital economy; analysis and synthesis – in the process of studying the individual essential characteristics of the category "cryptocurrency"; comparison – in the process of researching changes in the market capitalization of cryptocurrencies, the dynamics of the Bitcoin exchange rate, the dynamics of the market capitalization of cryptocurrencies and DeFi; graphic – to visualize the results of the study. Findings. The influence of environmental factors on the dynamics of the development of cryptocurrencies during 2021‒2023 has been determined. The directions of their development in the digital economy are substantiated, which include the correlation of the crypto market with traditional markets and the financial sector of the economy, the emergence of new types of cryptocurrencies that expand the range of offered market tools, the expansion of the scope of use of cryptocurrencies through the introduction of innovative technologies and tools. Alternative directions for investing in crypto assets have been identified. A comparative assessment of the risks of traditional investing and investing in cryptocurrencies was carried out. Originality. Determining the essential characteristics of cryptocurrencies, such as price volatility, investment attractiveness and riskiness, has been further developed. Prospective directions for the use of innovative tools in the crypto industry, in particular, decentralized finance (DeFi), are substantiated. Practical value. Conclusions and proposals substantiated by the results of the research can be used in the process of carrying out operations with cryptocurrencies and making decisions by market subjects regarding directions and instruments for investing in crypto assets. Key words: cryptocurrencies, market capitalization, rate dynamics, price volatility, innovative technologies and tools in the crypto industry, decentralized finance (DeFi), alternative investments in crypto assets.

The introduction of innovations makes it possible to ensure a stable economic situation for the business entity – both at the level of the national financial sector and in the international financial services market. The spread of financial technologies in the world continues to evolve through the emergence of new products and services. Despite different approaches to the development of enterprises and organizations in the financial sector, the common features remain the desire for innovation, ensuring the security of the financial system and protecting consumers. The contemporary tendency towards the introduction of flexible models of response to changes in the economic environment is one of the major strategies for creating favorable conditions for the development of innovative technologies in the financial sector. The aim of the publication consists in a theoretical generalization of the international experience in the implementation of financial innovations and to develop recommendations for its implementation in domestic practice. Financial innovation technologies are an important component of the development of the national economy of Ukraine. Our country has significant potential for the development of financial technologies, but its regulatory framework still needs significant changes to create a favorable environment for innovation. Unlike the UK or the EU, where regulations are aimed at accelerating the introduction of financial technologies, in Ukraine this process is just beginning. The article analyzes the international experience in the introduction of innovative technologies in the financial sector. The experience of fintech innovations in the USA, Great Britain, China, Singapore, South Korea is considered, the features of implementation and the possibility of their adaptation to activities of the business entities in Ukraine are determined. The existing regulatory obstacles to the possibilities of generation of fintech startups in Ukraine are identified. The National Bank of Ukraine (NBU), which is the key regulator of the national financial sector, is gradually introducing new rules and conditions for the development of financial technologies. However, at present, the limited legal framework for cryptocurrencies, blockchain technologies and other innovative solutions remains a problem, which hinders the development of such instruments. An analysis of the experience of implementing innovative technologies in the leading countries of the world proves that Ukraine already has a fairly flexible regulatory system, which creates potential advantages for the generation of financial startups, but at the same time there are certain risks of an imperfect regulatory system.