ALGORITHMIC METHODS OF PARALLEL PROCESSING OF BIG DATA IN PREDICTIVE ANALYTICS SYSTEMS
DOI:
https://doi.org/10.32718/agroengineering2025.29.232-241Keywords:
algorithmic methods, predictive analytics, GPU computing, scalability, artificial intelligence, machine learning, distributed computingAbstract
The rapid growth of data volumes and the increasing complexity of predictive and analytical models necessitate the efficient use of parallel processing in modern information systems. The aim of this paper is to substantiate algorithmic and architectural approaches to parallel processing of big data in predictive analytics systems, taking into account performance, scalability, and adaptability requirements. The study provides an analytical comparison of parallelism models, software frameworks, and computing architectures, and examines system-level constraints that affect the efficiency of predictive analytics systems in distributed and heterogeneous environments. It is shown that isolated scaling of computational resources does not ensure proportional performance gains, whereas the highest efficiency is achieved by hybrid configurations that combine multiple parallelism models with hardware accelerators. The scientific novelty of the study lies in the systematization of algorithmic and architectural approaches to parallel big data processing in predictive analytics with consideration of adaptability and system constraints, as well as in the formulation of an integrated analytical approach to combining software platforms and specialized computing architectures. The obtained results can be applied to the design of high-performance predictive analytics systems. The article also outlines directions for the further development of parallel predictive and analytical systems, in particular in the context of integrating machine learning methods, stream data processing, and dynamic management of computational resources. Special attention is paid to aligning algorithmic solutions with the characteristics of the hardware platform to minimize overhead costs and improve the energy efficiency of computations. The proposed analytical approach can serve as a methodological basis for building adaptive high-performance predictive analytics systems under conditions of variable workloads and heterogeneous computational environments.
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