JOURNAL SECTIONS Software Engineering Smart systems and the Internet of Things Applied information systems and technology Network and internet technologies Knowledge engineering Artificial and computational intelligence Mathematical foundations of information technology Machine learning and pattern recognition Information analytics and data analytics Digital technologies of IT project management Policy (purpose and objectives) of the scientific edition: The purpose of the edition is to highlight and discuss current research results in the field of information technology, as well as the practice of their application in solving economic, technological, and technical problems and development of methodologies and tools for the digital transformation of society. The scientific journal «Advanced Information Technology» is a peer-reviewed professional publication recognized in the following academic fields: F2 – Software Engineering F3 – Computer Science F6 – Information Systems and Technologies G5 – Electronics, Electronic Communications, Instrument Engineering and Radio Engineering Subject Area: Information Technology and Electronics The objectives of the edition are: Identification of the constituent elements and structure of the impact of information technology on the innovative development of the country. Formulation of new ideas, principles, and hypotheses for solving modern problems in the field of information technology and presentation of constructive methods of their solution. Coverage of aspects of the development of theoretical bases of new information technology and their experimental verification. Exchange of experience in the development and application of new models and methods of informatization. Editor-in-Chief Doctor of Technical Sciences, Professor, Dean of the Faculty of Information Technology, Taras Shevchenko National University of Kyiv, Vitaliy Snytyuk. Google Scholar ORCID: 0000-0002-9954-8767 SCOPUS e-mail: dekan.fit.knu@gmail.com
Київський національний університет імені Тараса Шевченка, 01033, Київ, вулиця Володимирська, 60
AN ANALYTICAL REVIEW OF CONTENT-BASED AND COLLABORATIVE FILTERING IN RECOMMENDER SYSTEMS
Background. With the rapid growth of digital content, recommender systems are becoming a key tool for providing personalized offers. They contribute to the discovery of new movies, music, and products, maintaining user interest in using platforms. The relevance of researching recommender system algorithms is due to the need to improve their work to satisfy individual user preferences. This paper presents a review and analytical study of recommender system algorithms. The purpose of this paper is to systematize, classify, and critically analyze two main approaches in recommender systems: content-based filtering and collaborative filtering. Methods. A review of existing recommender system methods, a comparative and analytical assessment. Results. The work analyzes recommender system algorithms. A formal definition of the recommendation problem is given, where user preferences are modeled as a functional dependence on object properties. Within the framework of content-based filtering, the use of classification algorithms, such as a Naive Bayes classifier and decision trees, as well as the Rocchio algorithm, which uses relevant feedback to update the user profile, is considered. The strengths and weaknesses of different similarity measures between vectors are analyzed. In collaborative filtering, the memory-based approach (user-based and item-based methods) and model-based techniques with an emphasis on the k-NN algorithm are investigated. To overcome the shortcomings of individual methods, a hybrid approach is proposed that combines their advantages. Methods for integrating systems into a hybrid model are presented, which allows improving the accuracy of recommendations. Conclusions. The results of the work highlight the features of the specified filtering methods, demonstrate the impact of the implementation of algorithms and input data on the accuracy of recommendations and response time. The analysis of shortcomings emphasizes the importance of the combined use of filtering algorithms to improve the efficiency of recommendation systems, which makes the hybrid approach a promising direction for further research and implementation.
collaborative filtering, content-based filtering, hybrid filtering, recommender systems, Rocchio algorithm, vector space model.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
EFFECTIVE APPROACHES TO TEACHING DESIGN PATTERNS: ANALYSIS OF CURRENT TRENDS AND EDUCATIONAL PRACTICES
Background. Determining effective methods for studying both classical and modern software design patterns, taking into account modern requirements for its development, is an urgent task. The basis of this study was the analysis of the latest trends in the practical application of various patterns in the process of program development, determining their popularity and searching for methods that will ensure more effective mastering of application architecture by future IT specialists. Methods. The research used methods of analyzing and summarizing information obtained from open sources, in particular, scientific publications, theses, articles, curricula, work programs of educational disciplines, syllabi and other regulatory documents. The work programs of educational disciplines (syllabuses) of several leading universities were analyzed, which allowed us to identify effective methods for studying software design patterns for future IT specialists. Results. The analysis of curricula showed that in Ukrainian universities, attention is focused mainly on the study of classical GoF design patterns, which somewhat limits the quality of students' preparation for working with the latest technologies. Compared to traditional teaching methods, in particular, lectures, laboratory classes, and the use of UML diagrams for visualization of program codes, the project method turned out to be more effective. Conclusions. An effective method of studying design patterns is to combine classical lectures with interactive classes and the use of the project method. This contributes to a better understanding of the material and prepares students to solve real problems, which are usually larger in volume and more complex than separate laboratory work, and require analysis and synthesis skills. Given the existing requirements of the labor market, it is advisable to update curricula by improving their content and structure, including by introducing more effective teaching methods.
design patterns, learning methods, project method
MANAGEMENT OF DATA CENTER SDN BASED ON THE MODIFIED DEVOFLOW APPROACH
Background. The paper investigates the features of implementing the SDN network management layer when servicing data center flows of different volumes based on Hedera technology, the OpenFlow protocol, and the DevoFlow approach. The distribution of processing functions, collection of statistical data, and the frequency of updating statistical data affect both the amount of service information and the efficiency and effectiveness of management in terms of distributing incoming traffic for load balancing. The aim of the work is to modify the Hedera approach by redistributing the functions of flow classification and adaptive control depending on the flow volume at the control level, as well as to develop an algorithm for determining the allowable time for sending statistics from switches to the SDN controller Methods. The method of system analysis and decomposition is used to study complex systems, methods of collecting data on the state of the network, as well as heuristic rules for determining the volume of service traffic. Results. The paper analyzes the features of construction and operation of technologies like Hedera and DevoFlow, recommend a modified architecture for building the control plane and identifies the main functions of software modules that can improve the efficiency of SDN controllers. Due to changing the procedure for collecting and processing service information on the equipment of the SDN network of data centers and returning to a centralized type of control. Approaches to the classification of incoming flows and their division into small “mice-flows”, medium “medium-flows” and large “elephant-flows” are determined, which allows further use of multi-path routing for more efficient use of network resources. Also, this article proposes an algorithm for determining the permissible time for sending statistics from switches (push-based method), which takes into account the dynamics of load changes in the lines in the network, the number of active sessions and is based on a heuristic rule for limiting the amount of service traffic. Conclusions. A modified architecture of the control level based on the DevoFlow approach has been developed and the main functions of the software modules of the SDN controller of the data center network have been determined. Also, some analytical dependencies have been developed to determine the approved time for sending statistics from switches to the SDN controller, taking into account the current state of network congestion. Additionally, specific experiments were conducted to confirm both the reliability of the chosen method for obtaining statistics and the proposed algorithm for determining the permissible time for sending statistics from switches to the SDN controller.
Software-defined Networking, data center, OpenFlow, Hedera, DevoFlow, elephant-flows, mice-flows, SDN controller, OpenFlow Switch, flow table.
A SYSTEM FOR DETECTING EXPLOSIVE OBJECTS BASED ON A MOBILE TRACKED ROBOT CONTROLLED FROM A QUADCOPTER USING MACHINE VISION
Background. The results presented in this article mark the beginning of the project team’s research in this direction. The experiments were conducted under laboratory conditions and focus on the general concept and identification of directions for further development. Future research is planned to be conducted in real or near-real conditions. The work proposes a system for detecting explosive objects based on the K158 pulse metal detector, a microcontroller, and the Keyestudio Mini Tank V3.0 mobile tracked platform. An integration with a quadcopter equipped with computer vision is proposed, enabling precise targeting and remote control. The system architecture, component interaction principles, decision-making logic, and a mathematical model of operation are described. Methods. The research employed methods of functional subsystem modeling, computer-based oscillographic analysis of electrical signals, mathematical modeling of the tracked robot’s kinematics in XY coordinates, and algorithmic design of a control automaton. To test the interaction with the quadcopter, virtual testing was conducted in a simulation environment using machine vision. Signal processing was implemented through microcontroller programming based on threshold filtering. Results. The results demonstrate a clear differentiation between signals in the presence and absence of metal. In the background mode, a stable zero line is observed, while detection of a target produces a series of pulses at a frequency of 125–150 Hz and a current of up to 625 mA. This ensures reliable real-time object identification. All components, including the power and wireless communication modules, showed stable operation. Machine vision-based targeting achieved an accuracy of ±20 cm. Overall, the system demonstrated reliability and efficiency in laboratory conditions. Conclusions. The proposed system for detecting explosive objects combines the advantages of analog sensing, digital signal processing, a mobile platform, and aerial coordination. Its simplicity, autonomy, and low cost make it suitable for use in high-risk areas.
explosive objects, Arduino, pulse metal detector, tracked mobile robot, quadcopter, computer vision, autonomous control, metal detection.
Motion-consistent temporal fusion for UAV detection and tracking
Background. Detecting and tracking Unmanned Aerial Vehicles (UAVs) in video streams is essential for modern air-space monitoring yet remains challenging because UAVs are small, fast and easily confused with birds or background clutter. Conventional detectors produce noisy, frame-wise boxes, while standard trackers still suffer from false positives and identity switches. The purpose of this study is to stabilize UAV detections by adding a motion-aware temporal fusion method to a mainstream detector-tracker pipeline. Methods. A detection-tracking pipeline was constructed using an RT-DETR (Real-Time Detection Transformer) and ByteTrack baseline, extended with a lightweight, training-free motion-consistent fusion (MCF) method. The method (i) aggregates bounding-box history over five frames, (ii) averages spatial and confidence values, and (iii) penalizes tracks whose short-term velocity or angular change exceeds empirically chosen thresholds. No appearance features or additional learning are required, so the solution runs in real time on a single GPU. Results. Experiments on a labelled UAV-video dataset show that the proposed method increases Multiple Object Tracking Accuracy (MOTA) from 0.533 to 0.591, precision from 73 % to 84 %, and reduces identity switches from 60 to 28 (a 53 % improvement in ID stability). Recall decreases slightly from 90 % to 76 %, reflecting a deliberate trade-off: the system filters unstable or non-UAV motion to improve track consistency and suppress false positives. The evaluation was performed on more than 1,000 video sequences, ensuring robustness across diverse flight environments. Conclusions. The motion-consistent fusion method significantly enhances both accuracy and temporal coherence while adding minor computational cost. It can be added into existing detection–tracking systems and is particularly suited for real-time UAV surveillance applications, though performance may degrade if drones execute extremely abrupt maneuvers outside the predefined motion thresholds.
Applied information systems and technology
UAV, Object Tracking, Object Detection, Fusion Algorithm, Motion Filter, RT-DETR, ByteTrack
MODEL FOR ASSESSING THE ROBUSTNESS AND SURVIVAL OF MOBILE ROBOTIC PLATFORMS TAKING INTO ACCOUNT RELIABILITY
Background. This publication develops a comprehensive model of reliability, robustness, and survivability of a mobile robotic platform (MRP) with a series-parallel architecture. The aim of the work is to perform mathematical modeling and analysis of the reliability of MRP components based on Weibull and exponential distribution laws, as well as to assess the impact of redundancy and structural redundancy on the overall system resilience to failures. It was established that the critical elements of the MRP are the chassis, motor, and controller, as they are connected in series and have a low level of fault tolerance without redundancy, unlike the sensor and communication components connected in parallel. Methods. Reliability calculation using series-parallel schemes; evaluation of mean time to failure (MTTF); modeling using Weibull and exponential distributions; sensitivity analysis to evaluate the impact of components on system reliability; assessment of system robustness and survivability under environmental changes or faults. Results. Based on the constructed models, numerical MTTF values were obtained for all MRP components. The highest fault-free operation times were recorded for the chassis (135,412 hours) and the battery (124,914 hours), while the lowest reliability was demonstrated by the Wi-Fi communication modules (80,000 hours) and BLE modules (70,000 hours). The MTTF for the entire MRP is 21,188 hours, approximately 2.4 years of continuous operation. Sensitivity graphs showed that the chassis, controller, and motor have the greatest impact on overall reliability. These are priorities for redundancy or the creation of degraded modes. The system robustness with one failure (Δ ≤ 1) was evaluated through the decrease in probability of fault-free operation. However, over time (beyond 100,000 hours), reliability drops below 5%, highlighting the importance of accounting for component wear. To increase survivability, the MRP should operate with up to two platform failures, plan routes based on reliability, support degraded mode, and dynamically redistribute tasks. Conclusions. The proposed model enables highly accurate reliability assessment of the MRP considering its structure, connection types, and functional features of components. The results indicate the need for redundancy of critical components and increased system robustness to ensure failure resilience. The presented methodology can be used for maintenance planning, lifecycle forecasting, and optimization of MRP operation in dynamic environments.
mobile robotic platform (MRP), reliability, robustness, series-parallel structure, component sensitivity, route optimization.
Optimization of metadata volume in modern information systems: methods, tools and algorithmic approaches
B a c k g r o u n d. The study of metadata volume optimization aims to achieve a balance between the sufficiency of description and system efficiency. Excessive metadata can overload systems, and insufficient metadata can complicate data access. At the same time, the growing volume of data complicates work with metadata, since its creation, storage and processing require significant resources. Metadata volume optimization has become an important task for organizations seeking to achieve effective information management. Challenges associated with metadata volume: redundancy, insufficiency, duplication, data dynamics, non-compliance with standards. M e t h o d s. The paper considers the theoretical foundations, practical methods, tools (Collibra, Apache Atlas, Talend Metadata Manager, AI algorithms) and the benefits of metadata volume optimization (cost reduction, improved productivity, improved data quality, flexibility and scalability, improved analytics). R e s u l t s. The paper proposes a comprehensive metadata optimization strategy adapted for the IT environment. It is shown that the use of a systematic approach, including analysis, standardization, automation and integration of the latest technologies, allows to significantly reduce costs and improve data management. An algorithm for optimizing the volume of metadata is presented, which can be adapted for various application areas, such as databases, content management systems or big data. The proposed algorithm takes into account the assessment of metadata usefulness using metric normalization to unify evaluation scales and determine the usefulness of each metadata element; metadata selection (filtering, clustering); metadata compression; automatic optimization using machine learning models and dynamic tuning; verification and adaptation. The algorithm can be expanded or modified depending on the specifics of the task. C o n c l u s i o n s. The proposed algorithm takes into account: metadata usefulness assessment using metric normalization to unify evaluation scales and determine the usefulness of each metadata element; metadata selection (filtering, clustering); metadata compression; automatic optimization using machine learning models and dynamic tuning; testing and adaptation. The algorithm can be expanded or modified depending on the specifics of the task.
metadata, optimization, management, information, dynamism, adaptability, relevance, performance, quality, algorithm, compression
Copyright (c) 2025 Advanced Information Technology https://creativecommons.org/licenses/by-nc/4.0
HYBRID CLOUD-BASED INTELLIGENT TRAFFIC MONITORING IOT SYSTEM FOR A RESIDENTIAL AREA
B a c k g r o u n d . This paper presents a new architecture for an intelligent transportation system (ITS) that leverages Internet of Things (IoT) technologies and the Azure cloud platform. The scientific novelty lies in the development of an architecture that integrates edge computing, cloud services, and machine learning algorithms for adaptive traffic management based on real-time data. The proposed architecture efficiently processes traffic flow information, performs modeling, and automatically adjusts traffic signals to reduce congestion. The effectiveness of the architecture has been validated through a series of experiments focused on vehicle recognition, traffic signal optimization, and real-time monitoring of the traffic situation. M e t h o d s . The methods used include computer simulation modeling for managing the intelligent transportation system, reinforcement learning for training the system, and computer vision techniques for vehicle recognition. R e s u l t s . The proposed ITS architecture is based on IoT technologies, enabling real-time data collection and analysis of road traffic. The developed system was tested in various urban areas with different levels of traffic load. The experiments demonstrated that t he system can adaptively adjust traffic signals based on traffic analysis, significantly improving road capacity and reducing congestion. C o n c l u s i o n s . The results of the experiments confirmed the effectiveness of the proposed intelligent transportation system architecture. Future research may focus on enhancing the system by incorporating more advanced artificial intelligence algorithms for automating traffic signal management decisions.
intelligent transportation system, cloud platform, transportation system optimization, IoT system architecture design.
THE CONCEPTUAL MODEL OF INTELLIGENT MANAGEMENT OF NETWORK RESOURCES IN THE IMPLEMENTATION OF THE SDN/NFV PARADIGM
B a c k g r o u n d . With the development and spread of network technologies, the problem of effective management of networks is becoming increasingly relevant. One approach to solving this problem is to use network virtualization, such as software-defined networking (SDN) and network functions virtualization (NFV), which allows to use of non-specialized hardware, including hardware on the x86 architecture, and makes networks more efficient due to the optimal use and distribution of resources. Due to this, it becomes possible to use own models and methods for managing networks. The purpose of the work is to find and develop a conceptual model of intelligent management of network resources in software-defined networks, which, unlike the existing ones, can dynamically allocate resources depending on the needs of the network and can adapt to possible minor discrepancies between the received data and real needs under the conditions of external and internal destabilizing factors. M e t h o d s . In this paper methods of neural networks, fuzzy logic methods as well as methods of computer simulation are used. R e s u l t s . In this study, it is proposed the conceptual model of network resource management based on a hybrid neural network using fuzzy logic and fuzzy output inference (ANFIS), which allows for dynamic response to network needs and is less sensitive to the rapid obsolescence of received data regarding its needs. The model was tested on a simulated communication network in the MATLAB environment in two scenarios with low and high network load. It showed a good result that allowed the virtual devices to handle requests without overloads and have a certain margin of computing resources for efficient work and possible minor growth of network needs. C o n c l u s i o n s . The developed model of network resource management in the MATLAB environment showed high efficiency, which proves the necessity and relevance of further research on using fuzzy logic in network management.
communication network, network resource management, fuzzy logic, neural networks, network functions virtualization, software-defined networking, ANFIS, MATLAB.
MICROSERVICE GROUPING FOR UNDERUSED RESOURCE USAGE
B a c k g r o u n d . The task of optimizing cloud system resource usage is of high priority, as this technology has become widely adopted and easily accessible. Efficient services are common to be created to beastly adapt to different types of products, e.g. serverless technologies and proprietary databases, and cloud-optimized software. Performance testing of cloud applications has become significantly easier with the emergence of Docker and Kubernetes, which has further increased the demand for cloud resources. Since a considerable amount of resources is spent on the development and maintenance of cloud infrastructure, it is essential to understand how these resources can be optimized. M e t h o d s . The basic idea is to reorganize cloud infrastructure so that the software is distributed into the server groups, not the serv ers in a complementary manner, meaning that the hardware load will be close to the full load. To form complementary groups, methods of clusterization (K-Means), multiple knack’s problem, sorting, binary search, shuffling and division based on mean and standard deviation are used. Additionally, FastDTW and Z-scale algorithms are proposed for visualization. R e s u l t s . The article examines the key characteristics of computer systems that can be measured and influenced. These characteristics include transmission channel bandwidth, delay-based performance evaluation, the amount of RAM and permanent storage, processing power, and the number of cores. An algorithm is proposed to identify complementary microservice instances that could efficiently uti lize server resources. Initially, microservice instances are classified by their capacity, considering small instances as a unit of capacity. Through analysis, the microservice instances are grouped into equivalence classes based on similarity. The instances are then sorted by the amplitude of resource usage. Ideally, instances with significant differences in load are combined with others that have similar amplitudes but opposite ph ases to maximize resource utilization. Combinations with instances that have low amplitude may also be appropriate. Within opposite classes, which differ in the activity phases of microprocessor instances, the algorithm searches for the first microservice instance that meets the conditions. To achieve this, statistics of combinations with all instances are stored until the first successful combination is found. Otherwise, the search continues till the least possible combination. The leftovers after the combinations search are expected. They are additionally divided using the extended extremums search with the mean and standard deviation search to increase their chances to find compliments. Finally, the small amount of microservices that didn’t find a match is combined using multiple knack’s problem. C o n c l u s i o n s . The conclusions of the article suggest an algorithm that will optimize the use of cloud system resources by effectively distributing the load between different microservices. Optimization means to maximize the use of server resources by filling its free time with other microservice and so reduce server downtime. This, in turn, will contribute to improving general cloud productivity and reducing maintenance costs of cloud infrastructures. This algorithm can be useful for cloud service providers and organizations that u se cloud environments to deploy their applications.
microservice grouping, CPU efficiency, energy consumption optimization, cloud systems, microservice architecture, technology cost reduction.
“THE MANAGER-DISPATCHER”:A DESIGN PATTERN FOR ENSURING ADAPTIVE BEHAVIOR OF EVENT-DRIVEN SOFTWARE SYSTEMS
B a c k g r o u n d . Adaptive behavior in modern software systems is becoming a key factor in their successful operation under external and internal destabilizing influences. Programs that work with critical data or perform essential operations must ensure continuity of service, despite failures, attacks, or errors. Various approaches are proposed to achieve this goal, one of which is the application of design patterns that ensure system reliability and adaptability. This paper presents the “Manager-Dispatcher” pattern, which combines the features of the “Publish- Subscribe” and “Strategy” patterns to enable adaptive behavior in software systems through event processing. M e t h o d s . The development of the “Manager-Dispatcher” pattern was based on modular design and dynamic event processing methods. The pattern provides an automatic strategy selection for module operation based on events occurring within the system. A theoretical analysis of existing approaches to adaptive behavior in systems was conducted, leading to the creation of a new pattern that enables dynamic strategy changes in modules in response to environmental changes determined by system events. Several hypothetical application scenarios were considered to illustrate the pattern’s functionality, and an example software system utilizing the pattern was developed and described. R e s u l t s . The developed “Manager-Dispatcher” pattern allows software modules to automatically adapt their operational strategies based on system events. Key advantages of the pattern include modularity, extensibility, and adaptive behavior. The pattern may be particularly useful in embedded systems, real-time systems, and interactive interfaces, where fast and flexible responses to events are essential. C o n c l u s i o n s . The “Manager-Dispatcher” pattern offers a promising approach to the design of event-driven adaptive software systems. With its ability to dynamically change operational strategies, the pattern ensures a high degree of flexibility in dynamic environments. Future research will focus on improving the pattern and developing tools to facilitate its implementation and testing. This proposed approach supports the development of modular and adaptive systems capable of maintaining stable operation even under complex conditions.
design patterns, adaptive behavior, event processing, autonomous systems.
DEVELOPMENT OF A NEW INDICATOR OF FUNCTIONAL RELIABILITY AND ITS EVALUATION USING MULTIVARIABLE POLYNOMIAL REGRESSION
B a c k g r o u n d . Functional The functional stability of distributed systems is becoming increasingly significant with the advancement of information technologies. Consequently, the formalization of this concept has gained relevance. Mathematical formalization of functional stability in the form of its indicators and criteria has been underway for several decades. Functional stability indicators play a key role in this process, and many such indicators have already been formulated. However, a major drawback of most of these indicators is that they are not only computationally complex but also dependent on numerous other parameters, or they fail to comprehensively describe the functional stability of the distributed system in question. In this paper, a new functional stability indicator is introduced that avoids the second of these drawbacks. The first drawback is addressed through the use of an estimation method based on multivariate polynomial regression. M e t h o d s . The study utilized methods of computer modeling and approximation techniques. R e s u l t s . A modification of an existing indicator, known in the literature as the probability of reliability, was chosen as the method for developing the new functional stability indicator. By making certain assumptions and applying transformations, a measure was obtained that possesses certain desirable properties, namely: this measure lies strictly within the interval from zero to one, and the larger it is, the more functionally stable the distributed system under consideration can be deemed. However, the resulting functional stability indicator requires extensive calculations, prompting an attempt to estimate this indicator using approximation methods. This study explored the potential of applying multivariate polynomial regression. According to computer modeling, to achieve an average accuracy of two percent, it is sufficient to use a five-dimensional polynomial regression of the fourth degree. Increasing the degree of the five-dimensional regression model beyond this does not result in significant error reduction. C o n c l u s i o n s . The functional stability indicator introduced in this study provides a convenient means for investigating the functional stability of distributed systems. However, it demands a significant amount of computation. For this reason, a method for estimating the introduced functional stability indicator has been presented, which allows for relatively accurate computation of this indicator.
functional stability, indicator, approximation, optimization, regression, multivariable functions.
HYBRID METHOD FOR HORIZONTAL AND VERTICAL COMPUTATIONAL RESOURCE SCALING
B a c k g r o u n d . Increasing the efficiency of cluster computing resources while maintaining the established QoS levels is a critical task in IT infrastructure management. Dynamic management of computing resources, in particular vertical and horizontal scaling, are tools that allow automating the processes of adapting applications to dynamic loads. The aim of the work is to improve the efficiency of existing scaling methods by combining them. M e t h o d s . A hybrid scaling method using the coordination module is proposed. This module coordinates the operation of vertical and horizontal scaling components based on the given constraints, priorities, and the current state of the system. The coordination module aims to increase efficiency of the both components and prevents inconsistency in the combination of the number of instances and resource requests for each instance. R e s u l t s . To achieve the given objective, a hybrid method of vertical and horizontal scaling using priority-based component coordination was developed. Priority configuration affects the order of components operation. In the case of horizontal-vertical order, the non-prior vertical component does not affect configuration of the prior horizontal component. The developed method is evaluated based on modeling the operation of an application with a load containing a constant seasonality. The experiments demonstrate a 65% reduction in the unprofitable reservation of cluster computing resources compared to static requests. C o n c l u s i o n s . The developed method can be used to increase the efficiency of resource utilization in clusters under dynamic loads compared to basic scaling methods. In further research, it is necessary to evaluate the method using real infrastructure. It is also necessary to investigate the work of a hybrid method using a predictive approach
information technologies, information systems, resource management, scaling, IT-infrastructure, vertical scaling, horizontal scaling.
USING AND ANALYSIS OF FORMAL METHODS FOR EVALUATING THE RELEVANCE OF AUTOMATICALLY GENERATED SUMMARIES OF INFORMATIONAL TEXTS
B a c k g r o u n d . The article reviews existing approaches to evaluating the quality of automatically generated summaries of informational texts. It provides an overview of automatic summarization methods, including classical approaches and modern models based on artificial intelligence. The review covers extractive summarization methods such as TF-IDF and PageRank, as well as graph-based methods, specifically TextRank. Special attention is given to abstractive approaches, including Generative Pretrained Transformer (GPT) and Bidirectional and Auto-Regressive Transformers (BART) models. The quality of generated summaries is evaluated using quantitative metrics of summary relevance, particularly ROUGE and BLEU. M e t h o d s . The article analyzes several approaches to automatic text summarization. Classical extractive methods, such as TF -IDF, calculate the importance of terms based on their frequency within a document and across a collection of documents. PageRank and TextRank utilize graph models to determine the significance of sentences based on the connections between them. Abstractive methods, s uch as GPT and BART, generate new sentences that succinctly convey the content of the original text. The effectiveness of each approach is assessed usi ng ROUGE and BLEU metrics, which measure the overlap between automatically generated summaries and reference texts. Particular a ttention is given to analyzing their accuracy, flexibility, resource requirements, and ease of implementation. R e s u l t s . The results of the study show that ROUGE metrics demonstrate good accuracy in measuring n-gram overlaps (sequences of n words), while BLEU is effective in machine translation tasks but may not account for certain syntactic features of the text. The evaluation of automatic summarization methods using these metrics revealed that extractive summarization methods, such as TF -IDF, are effective for processing simple texts but may lose important context in complex texts. PageRank and TextRank consider the connections between sentences but may produce less relevant results for texts with weak structural connections. Abstractive models like GPT and BA RT provide a more flexible approach to summarization, creating new sentences that better convey the meaning, though they require significant computational resources and are complex to implement. C o n c l u s i o n s . Combining classical and modern methods of automatic text summarization allows for achieving higher quality results. It is important to consider the specificity of the text and the requirements for the final outcome, adapting the selected approa ches and metrics according to the task.
automatic summarization, extractive methods, abstractive methods, GPT, BART, ROUGE, BLEU, TextRank, PageRank, TF-IDF.
USE OF ANT COLONY OPTIMIZATION ALGORITHM FOR SOLVING FUZZY PROBLEM OF TRAVELING SALESMAN
B a c k g r o u n d . The method of finding the optimal route length for the traveling salesman problem in the case of determining the time of movement between cities in the form of fuzzy trapezoidal numbers is formulated and given. The purpose of the work is to devel op an algorithm based on the optimization of an ant colony and use this method to solve problems of a traveling salesman with a sufficiently large number of cities in the transport network. M e t h o d s . The method based on the ant colony optimization algorithm was used. R e s u l t s . In order to achieve the goal, a scheme for the implementation of the optimization algorithm is proposed, which, under the condition of a small number of iterations, allows obtaining results close to optimal solutions in the vague problem of the traveling salesman. The proposed approach can be used to find a rational path in situations with an imprecisely specified duration of movements between cities. It is shown that the selection of the main parameters of the ant colony optimization algorithm does not significantly affect the quality of the obtained approximate solution. Examples of the use of the algorithm confirm the constructiveness of the approach to solving the traveling salesman problem in the case of a vaguely specified duration of movements. C o n c l u s i o n s . A scheme for the implementation of the ant colony optimization algorithm for finding the best path in the problem of a traveling salesman with variable duration of movements between cities is proposed, a computer program has been developed that allows solving various logistics problems, which are based on the problem of a traveling salesman with vaguely defined movement parameters in the transport network.
fuzzy traveling salesman problem, ant colony optimization method, trapezoidal fuzzy numbers, defuzzification, performance evaluation.
THE INTELLIGENT CONTROL OF THE CITY-FARM MICROCLIMATE BASED ON THE Q-LEARNING ALGORITHM
B a c k g r o u n d . In the context of the rapid development of urban farming and the growing interest in sustainable food production, microclimate management is becoming a key aspect to achieve optimal plant cultivation. Optimum management of temperature, humidity and light can help use limited space more efficiently, increasing yield per unit area. Climate control systems that allow you to create optimal conditions for plants allow you to increase production in a limited area. The purpose of the study is to make informed decisions in the climate control system based on reinforcement learning algorithms, in particular Q-learning, to increase the productivity and efficiency of growing microgreens in urban farming. M e t h o d s . In order to make informed decisions in the climate control system, the article examines the Q-learning algorithm, which consists of such stages as determining different climatic states of the system; selecting the action to be performed based on the current state of the system and a utility estimate that is calculated based on the Bellman equation. A microclimate management model was developed and implemented, which uses the Q-learning algorithm to optimize climate parameters. The research methodology included simulation of various environmental conditions, model training based on collected data and experimental testing in real conditions of urban farming. R e s u l t s . Experimental simulations using the Python programming language with TensorFlow, PyTorch and scikit-learn libraries confirmed the effectiveness of applying the Q-learning algorithm in the climate control system to increase the productivity and efficiency of growing microgreens. To ensure that the system has reached the desired state, strategies such as monitoring the actual parameter values using IoT sensors of the climate control system, analyzing the obtained Q-table values, and setting learning stopping criteria are used. The results of the program are transmitted to the actuators via the Wi-Fi data network using the ESP8266 microcontroller, which is used as a Wi-Fi module for the Arduino microcontroller. C o n c l u s i o n . The use of a climate control system with the Q-learning algorithm in urban farming contributes to the achievement of greater productivity, efficiency and stability of plant cultivation, which is reflected in the improvement of the results of plant cultivation.
distributed systems, IoT technologies, cloud computing, Q-learning, monitoring.
GENERAL-PURPOSE TEXT EMBEDDINGS LEARNING FOR UKRAINIAN LANGUAGE
B a c k g r o u n d . Learning high-quality text embeddings typically requires large corpuses of labeled data, which can be challenging to obtain for many languages and domains. This study proposes a novel adaptation of cross-lingual knowledge transfer that employs a cosine similarity-based loss calculation to enhance the alignment of learned representations. M e t h o d s . The impact of teacher model selection on the quality of learned text representations is investigated. Specifically, the correlation between cosine similarity scores among vectors of randomly selected sentences and the transferability of representations into another language is explored. Additionally, recognizing the need for effective evaluation methodologies and the limited availability of Ukrainian resources within existing benchmarks, a comprehensive general-purpose benchmark for assessing Ukrainian text representation learning is curated. R e s u l t s . A cosine-similarity based loss calculation leads to 14.2% improvement in absolute Normalized Mutual Information (NMI) score compared to using mean squared error loss when distilling knowledge from the English language teacher model into Ukrainian student model. The findings demonstrate the strong correlation between the distributions of cosine similarities of the teacher model’s representations of random sentences with the quality of learnt text embeddings. Pearson’s correlation between “90th percentile of cosine similarity scores distribution” and “Average NMI score” is -0.96, which is a strong negative correlation. C o n c l u s i o n s . This research advances information theory in cross-lingual knowledge distillation, illustrating that cosine similarity-based loss functions are superior in performance. It underscores the importance of selecting the teacher model with wide distributions of cosine similarity scores. Furthermore, a pioneering broad-scale benchmark, covering five distinct domains for Ukrainian text representation learning is introduced. The source code, pretrained model, and the newly created Ukrainian text embeddings benchmark are publicly available at https://github.com/maiiabocharova/UkrTEB.
Natural Language Processing, text embeddings, Deep Learning, Data Mining, multilingual language models, knowledge transfer, domain adaptation.
Formation of the IT project team based on the competence characteristics of the staff
Background. The paper deals with the problems of IT project team formation using staff competences assessment. Methods. Multi-agent method based on competences matrix analysis is used. Results. The task of forming a project team to develop and implement IT systems in terms of limiting the number of human resources is described. Based on the definition of competency indicators of employees of the company or contractors involved in the implementation of project work, a method of forming a project team involves analysis of competency matrices. When attracting employees to project work that requires the existing competencies and the appropriate level of these competencies, it is proposed to use the values of the characteristics of staff for the selected functionality. On the example of a company for implementing IT systems, an algorithm for forming a project team based on the values of functional competencies is proposed. Forms for the formation of the structure of project commands according to functional tasks of the project and minimum-admissible levels of sizes of characteristics of qualification for performance of these tasks have been designed. Matrices of elements and indicators of labor resources are constructed to determine the possibilities of involving them in the project teams as executors. An algorithm for generating project team members based on the requirements for project tasks and indicators of human resources competence has been built. The forecast of indicators of the probability of involvement of each separate labor resource in the project team structure for development and introduction of information systems is performed. Сonclusions. With account of the practical implementation of the method, the conclusion is made that proposed method allows one to not only form project teams but also pose problems for the company leadership as to either increasing qualification of their staff or searching for new personnel with proper qualifications.
Digital technologies of IT project management
project, workforce, project team, competence, competence matrix.
Modeling of the Ukrainian scientific space based on the analysis of data on the defense of dissertations
Background. The paper considers the concept of scientific space and the peculiarities of its formation in Ukraine. Methods. Methods of heuristic analysis of weakly-structured information are used. Results. The structure of information about defended theses was studied. Heuristics, which, firstly, contribute to the structuring of the existing weakly structured information, secondly, supplement the information that is not enough to formalize the problem to known mathematical models, thirdly, declare the position of the authors a priori, mark the outline of the further presentation of the material and research limitations, which the authors limit themselves are introduced. Approaches to the analysis of theses defended in Ukraine during 1993-2020 are presented. Parameters of defended theses whose values are available in open-access databases and identifiers assigned to them are listed. An analysis of Ukrainian cities in which a large number of theses were completed and defended was carried out. Such cities are defined as scientific centers. A matrix of statistics of correlations between scientific organizations of Ukrainian cities was built. An illustration of the correlations between the cities of Ukraine is provided, and the diagram of these correlations is displayed on the map of Ukraine. In this way, the preliminary structuring of the scientific space in terms of preparation and defense of theses was carried out. Models were developed and directions for further research of the scientific space of Ukraine were determined. Based on text analysis, it is proposed to identify keywords in abstracts of theses and to determine research directions by analyzing the degree of similarity between sets of keywords related to theses. Сonclusions. Implementation of the research results will foster versatile development of the scientific space in Ukraine and strengthen its scientific potential.
Information analytics and data analytics
scientific space, weakly structured system, heuristics, thesis, adjacency matrix, scientific centers, research topic, formalization.
Recurrent algorithm of telecommunication systems and networks design
Background. The Erlang B-formula that establishes a relationship between probability of dropped calls, call intensity in a certain link and available communication channels in this link is widely used in telecommunication networks’ design and calculations. In a great number of applications, a problem emerges of finding required number of channels in a link for set call intensity to satisfy required probability of dropped calls. Methods. Methods of computer simulation as well as complex systems analysis. Results. With account of the fact that the number of channels enters Erlang B-formula, firstly, as upper index of summation, secondly, exponent of the exponent function and, thirdly, in the factorial, the solution of the problem is possible graphically. This approach includes calculation of the dropped calls probability for a certain range of numbers of channels to determine the first channel’ number for which dropped call probability results in a value below the threshold. The paper shows that for big values of the number of channels and call intensity direct use of Erlang B-formula on a computer leads to quick bit grid overflow and inability to fulfill required calculations. To overcome this problem, a recurrent algorithm is proposed to resolve the task, which is derived from Erlang B-formula and due to its structure does not suffer from the above limitations while performed on a computer. Сonclusions. Research results of the proposed algorithm demonstrate its high efficiency in implementation in Matlab environment. It is thought that the algorithm can find wide application in telecommunication networks design.
telecommunication network, Erlang B-formula, recurrent algorithm, Matlab.
Computer simulation model of signal propagation processes in coaxial cable
Background. The paper studies processes of signal propagation in a coaxial cable as a type of artificial long lines. Utilization of analytical methods as well as natural experiments for analyzing these processes is rather difficult and not sufficiently descriptive. The aim of the paper is to develop a simulation computer model of coaxial cable represented as an artificial long line and to use this model for studying processes in it. Main attention is paid to clarify impact of irregularities in the line and matched signal transmission from the source to the load with minimal losses. Methods. Methods of computer simulation as well as complex systems analysis are used. Results. To achieve the set goal, the paper solves the tasks of a coaxial cable equivalent circuit design as an artificial long line, which includes signal source with a certain internal impedance, the line itself with a possibility to vary its parameters and the load; representation of the processes in the developed line with the help of Ohm’s and Kirchhoff’s laws in terms of differential equations of continuous time; transition from the system of differential equations to the one of difference in order to use them for computer simulation; design of a computer simulation model on the base of the obtained system of difference equations; and trial of the computer simulation model in the scenario of signal propagation in the long line with irregularities. Computer simulation results that are obtained in the paper completely correspond to the theoretical notions related to the processes in long lines in different modes of their operation. The results have allowed one to assess numerically and qualitatively, and most impotently descriptively, impact of irregularities in the line on the optimal signal transmission in it. Сonclusions. Developed computer simulation model of a coaxial cable as an artificial long line can be used for tuition processes at universities and for design and modernization of these devices. Keywords: simulation computer model, coaxial cable, artificial long line, line irregularities, matched signal transmission
simulation computer model, coaxial cable, artificial long line, line irregularities, matched signal transmission.
The method of distance measurement in wireless sensor networks by means
Background. Currently, there is a problem of limited resources of individual nodes of sensor networks, to solve many problems (including positioning), there is a need to develop methods for joint work of all nodes of the network. One of such tasks is the task of localization of nodes of the sensor network with self-organization. It consists in determining the coordinates of individual sensors without the use of external infrastructure. The problem of localization has been extensively studied in the past, as information on the location of objects or people is important in many applications, and a large number of systems have been developed to address them. The most famous of these is the Global Positioning System (GPS). However, the GPS approach cannot be applied to WSN due to its requirements for a large number of additional infrastructures (e.g. satellites). Methods. In this paper methods of complex systems analysis and synthesis as well as computer simulation are used. Results. In this study, it is proposed to use the method of measuring the distance between objects, based on the time of arrival of the signal. The estimation of error of measurement of frequency, at measurement of high and low frequencies is given. It is determined that in the study of periodic processes in a wide range of frequencies to achieve a given accuracy, it is advisable to use a digital frequency meter in the high frequency range, and in the low frequency range to move to period measurement. Research has been conducted and graphs of quantization errors in frequency and period measurement have been constructed. An algorithm of microprocessor frequency meter operation is built, which allows studying periodic processes in a wide range. The block diagram of the microprocessor frequency meter is offered, which allows implementing the given algorithm. Сonclusions. Implementation of the designed microprocessor frequency meter allows one substantially increase efficiency of wireless censor networks due to more precise measurements of the network elements positions.
wireless sensor network, localization, distance, microprocessor frequency meter, distance measurement error.
The structure and functions of the integrated DSS in the multi-stage rolling production management processes
Background. The problem of a two-level information technology development of decision-making support in terms of metallurgical rolling production was considered. Both levels of individual technological operations and the entire production process are included, basing on economic indicators of the last one. Justification of the decision-making support system (DSS) structure and its functions is mainly focused on. Methods. Methods of complex systems analysis and synthesis as well as complex control systems design are used. Results. A modular structure of an automated DSS for managing metallurgical production, based on BOF steelmaking, is proposed, which contains seven separate software modules and six databases. All calculation, optimization and recommendation processes are implemented in separate modules of the DSS. An algorithm and scheme of interaction between modules and enterprise databases are developed. Input and output information for each of the modules is formulated. It is assumed that each of the selected modules, in addition to the organization of the input-output interface, performs the solution of a certain optimization problem aimed at improving the global economic criterion of production efficiency. All the results of the individual modules performance of the developed system are displayed through the appropriate interfaces and are of a recommendatory nature. The operator might accept them for implementation or perform actions at his own discretion. Both decisions, proposed and adopted, are stored in the respective databases. Сonclusions. In general, use of the proposed DSS in rolling production with the wide assortment of the products allows one to decrease cost of manufacturing of the molten steel by 2,5-3,8%, as well as that of the final products – by 4,25 – 4,8% due to the additional decrease in unmeasured byproducts and the number of the cut-off parts.
information technology, DSS, rolling production, decision-making, integration, databases, information interaction, system structure.
A HYBRID QUANTUM-PERFECTED MODEL OF ARTIFICIAL INTELLIGENCE IN THE PROBLEM OF AUTOMATIC RECOGNITION AND FAST CONVERSION OF UNSTRUCTURED TEXT INFORMATION INTO SPATIAL
Abstract DOI: https://doi.org/10.17721/AIT.2023.1.06 Background. Efficiently converting large amounts of unstructured text data into spatial information is crucial for managing water distribution systems. This allows for the conversion of extensive sets of text information, such as reports, orders, letters, and other documents, into point classes of spatial objects in geographic information systems. To tackle this challenge, a promising new approach involves combining hybrid quantum-classical neural networks with geo-information technologies. Methods. The study utilized quantum-enhanced hybrid neural networks in combination with GIS methods to identify named entities such as personal accounts and balance sheet objects of Kyivvodokanal by their addresses and geocoding. This information was then published on a geoportal using the ArcGIS Enterprise platform in real-time, which holds great promise for effective water management. The performance of the developed model was evaluated by accuracy indicators, recall parameters, and weighted harmonic average of accuracy and recall. Results. The obtained results indicate that the developed hybrid quantum-classical model of artificial intelligence can be successfully applied to transform large volumes of unstructured textual information into spatial information. The model was integrated into GIS using ArcGIS Enterprise. By combining the obtained point classes of spatial objects with already existing data, methods of spatial connections, an interactive map with an update interval of every five minutes was developed. Conclusions. Taking advantage of quantum computing and combining it with classical hardware and classical AI models, it became possible to achieve similar and even better performance in various tasks compared to state-of-the-art methods. Quantum natural language processing is a promising new field that has the potential to revolutionize the way one analyzes and understands human language.
QNLP, hybrid quantum-classical neural networks, geocoding, geoparsing, geographic information systems, ArcGIS, entity recognition, unstructured textual information, variational quantum igniter, QLSTM, spatial objects.
Optimization of multi-request single-processor computing
Background. The effectiveness of multi-query execution in single-processor computer system databases is considered. One of the methods of increasing the performance of databases of computer systems is the simultaneous execution of several queries that form a multi-query. Methods. Methods of analysis and criterial optimization are used in the paper. Results. The paper analyzes the processing of a conjunctive multi-query (queries are formed by the conjunction of elementary queries, from which a number of elementary queries are repeatedly included in the queries). With the growing importance of operational analytical data processing, the technique of more complex query optimizations has become crucial. In order to be effective, optimizers must adapt to new operators, changes in cost estimation methods, etc. The query execution time is usually used as a criterion for multi-query optimization, while subdividing the time spent working with data in RAM, buffer, and external memory. An optimization plan for conjunctive multi-query execution time when accessing the database based on the ordering of elementary queries is proposed. Two methods of performing a conjunctive multiquery are considered: independently of each other and jointly for unordered and ordered data. It is shown that the execution time of a conjunctive multi-query with joint processing is not always less than with independent processing. The conditions under which the joint processing of a conjunctive multi-query ensures no longer execution time in relation to independent processing are proved. Сonclusions. Implementation of the proposed optimization procedure allows one to increase efficiency of single-processor computer systems that support data bases.
optimization, query, multi-query, independent, concurrent processing, criterion, time, unordered, ordered data, database, performance
The architecture of the IoT ecosystem of the rehabilitation center for the restoration of the musculoskeletal system
Background. A study of the use of IoT technologies in modern medicine, primarily in Ukraine, was conducted. Based on the analogs of the existing systems considered, taking into account the entire complex situation, the number of people in need of rehabilitation is only increasing, and there are simply not enough centers for such assistance. Rehabilitation doctors spend a lot of their time, while not everyone can get the necessary help due to the lack of specialists. As a result, there is a need to create your own IoT system to automate and optimize treatment processes. Methods. Methods of analysis, synthesis and complex systems design are employed in the research. Results. The architecture of the IoT ecosystem of the rehabilitation center for recovery after injuries, traumas, assaults and operations was developed. The project architecture consists of simulators with sensors and a Wi-Fi module for data transmission to the cloud with a database and cloud computing. The end user, i. e. the doctor, can access this data through the app, process it, monitor performance and enter a training plan for each patient. It should be noted that each patient will be issued a card with an RFI tag or an IoT beacon. The card identifies the patient on each simulator. The simulator is adjusted according to the indicators. The indicators of a specific person’s condition are transferred to a database for tracking by a doctor. Сonclusions. Implementation of the proposed solutions is expected to allow one to substantially increase the quality of medical treatment provision to the population in Ukraine.
IoT architecture, rehabilitation center ecosystem, fitness bench, transducer, rehabilitation.
The use of drones in transport infrastructure
Background. The article is devoted to the real possibilities and prospects of creating and using unmanned aerial vehicles in road and railway infrastructures. As part of the conducted experiment, the task of creating a drone was set using the simplest means of development in laboratory conditions. Methods. To assess the viability of the proposed solutions, the method of the natural experiment has been used. Results. The drone created as a result of the experiment is able to automatically stabilize its position, and a receiver board can be installed on it, which will enable remote control. In the future, additional modules can be installed on the quadro copter using the deep learning mechanism. And the use of an intelligent pattern detection and recognition system based on effective digital data processing algorithms will allow to significantly reduce the time for data processing, obtain more accurate results and ensure access to information in the shortest possible time, which will be another factor contributing to the active implementation of unmanned technologies. Сonclusions. Thanks to the ability to obtain various data, unmanned aerial vehicles will be able to significantly reduce the costs of solving various tasks in the near future and become indispensable assistants in the transport infrastructure sector.
machine learning, transport infrastructure, unmanned aerial vehicle, drone, data processing and analysis.
Elements of neural networks technology for analyzing the attitude of Twitter users towards brands
Background. The article is devoted to the issues of effective organization of collection and information analysis about the attitude of Twitter users to brands in the software application form. Issues such as research into modern means of collecting and analyzing information are considered; definition of the functionality that the application should implement; analysis of architectural solutions and selection of software necessary for its implementation. Methods. When conducting research, marketing theory is used in the field of collecting information about consumer opinions, research on methods of information analysis for the purpose of classifying consumer mood, empirical analysis and synthesis of architectures used in the creation and comparison of neural network models for text classification, development and construction of own model for classification. Results. As part of the task of software implementation of tweet text analysis, the architecture of convolutional and recurrent neural networks was investigated, a comparison of various hyper parameter values of neural networks was made, in particular, activation functions, loss functions, the number of learning epochs, the number of network layers, a comparison of different Python libraries for processing natural languages in the context of tweet evaluation. Сonclusions. The practical significance of the study is the creation of a software tool for effective analysis of Twitter users’ attitudes towards brands, which can serve to improve the effectiveness of marketing activities of brands.
Twitter, information analysis, text classification, sentiment analysis, neural networks, software application, Python libraries.
A conceptual model of functioning of e-commerce project
Background. Management of e-commerce projects, which is aimed at individual orders, is gaining more and more value. This is due to the transformation of business and consumer demands. The results of the analysis of literary sources made it possible to establish the openness of the issue of combining the project approach and electronic commerce tools. It has been established that for a rational combination it is appropriate to form project management tools and e-commerce tools (platforms, design, analytics, etc.) that satisfy the project conditions and business models in the network. Methods. Two stages of selection of tools for online projects of personalized orders are proposed. Expert methods are the basis of the first stage of tool selection. Experts are IT and production specialists. The next stage of the analysis of tools that will be timely and ensure the competitiveness of the project in the conditions of electronic commerce is the processing of statistical information of the project, which is proposed to be selected from the database and knowledge base of the project. Results. The triad of knowledge in the development of an online project of personalized orders is presented; a conceptual model of e-commerce project management aimed at individual orders. This model is based on information analytics of the information space of the project. The work presents an example of the application of the presented concept in online projects of personalized orders in the confectionery sector. Сonclusions. The implementation of the conceptual model will increase the effectiveness of the implementation of small business creation projects or the development of existing ones.
e-commerce projects, project management methodology, tools, information space, expert method.
Project management models to create IT company in the field of Data Science
The basis for the emergence of projects to create IT companies in the field of Data Science is considered. The relevance of such projects is substantiated. The feasibility of their study using models and methods of project management is proven. Literary sources in three directions are analyzed. Such areas include classical project management, flexible approaches to project management, value-oriented management. Insufficient research of the described subject is proved. Groups of models and methods are described that describe the relevant areas of project management knowledge that will be necessary and minimally sufficient to develop the scientific basis of the IT company creation project in the field of Data Science. Namely: project team management, project value management, project concept development, project content management, project communications management, flexible project management tools. In the analysis of each area, the relevant models and methods are highlighted. Their applicability to the researched project is analyzed. A model for selecting a set of models and methods within the identified key knowledge areas for the research project in the form of a convolution of criteria is proposed. SWOT-analysis of the proposed approach was performed. The strengths, weaknesses, opportunities and threats associated with the proposed approach are highlighted. Conclusions to the study are formulated. Prospects for further research in the chosen direction are outlined.
information technologies, project management, Data Science, IT company.