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• MODIFIED DISTRIBUTED DATA PROCESSING ARCHITECTURE FOR GEOINFORMATION SYSTEMS

  M.Y. Polenov, D.A. Ivanov

  2021-02-13

  Abstract ▼

  The paper proposes a modified distributed data processing architecture based on the clientserver
  model, as one of the options for implementing a software application of a geographic information
  system. The review of existing geographic information systems and their classification
  from the point of view of architecture demonstrated the prospect of using a distributed architecture.
  However, systems developed on the basis of a traditional distributed architecture face problems
  displaying processed 3-dimensional data in real time on computing devices with low performance.
  In this regard, the purpose of this work is to develop and study a modified architecture of
  geographic information systems, which allows to reduce the requirements for computing devices of
  clients. The relevance of the research topic lies in the fact that currently there are devices that can
  support only thin clients, which often have small functionality and are not able to solve heavy
  computing tasks. The article discusses the features of the structural and software implementation
  of a geographic information system based on the traditional architecture and the proposed modified
  distributed architecture. The results of experiments carried out on two developed software
  applications with different architectures are presented. The software implementation of the modified
  architecture and the results of experiments have shown the feasibility of its application for
  geographic information system systems on user computing devices with low performance.
  The proposed architecture can be used in other distributed systems as well. Especially in those
  where the task is to display three-dimensional information on thin clients.

• FORECASTING STUDENT MOVEMENT USING MACHINE LEARNING AND TIME SERIES ANALYSIS

  Mirziyod Adkham ugli Radjapov , К.D. Chemukhin , L. E. Petrosyan

  134-151

  2026-07-07

  Abstract ▼

  Managing student mobility amid demographic fluctuations and the digitalization of higher education is becoming a key factor in university sustainability, affecting both financial performance and the quality of the educational process. The increasing complexity of processes such as admissions, withdrawals, academic leaves of absence, transfers, and reinstatements requires a shift from expert assessments to formalized models and predictive analytics based on the processing of large datasets. The aim of this study is to develop and evaluate the effectiveness of a model for forecasting student population dynamics based on machine learning algorithms and using time series analysis. Aggregated statistical data on student mobility at Russian and Chinese universities for the period 2013–2024 were used as the empirical basis, which allowed for consideration of both the structural features of national higher education systems and long-term trends and anomalous events (including the impact of the COVID-19 pandemic). The methodological framework includes a dynamic student cohort balance model in the form of a system of recurrent equations describing transitions between academic years and enrollment statuses, and an additive Prophet model used for independent forecasting of key flows (admissions, transfers, withdrawals, academic leaves of absence, reinstatements) as separate time series. The software implementation is based on the FastAPI–React stack, utilizing the SQLAlchemy ORM layer and mechanisms for caching the results of predictive calculations, which ensures high performance when processing queries. Experimental results on real data demonstrate the robustness of the developed model to nonlinear changes in the input series and confirm the feasibility of integrating machine learning into the student movement management system. The practical significance of this work lies in the creation of an information and analytical system that provides automated monitoring and forecasting of student movement trajectories between courses and statuses, enabling universities to transition from reactive to proactive planning of admissions campaigns, classroom allocation, and the distribution of personnel and infrastructure resources.