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HYBRID MODEL FOR STORING AND PROCESSING TEMPORAL DATA IN DIGITAL TWIN SYSTEMS OF INDUSTRIAL OBJECTS

Andreev A. A.  (Lead Specialist, The Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russia )

The article examines modern approaches to storing and processing temporal data in digital twin systems for industrial objects. The increasing volume of data generated by intelligent sensors and monitoring systems, as well as the widespread adoption of digital twin technologies, necessitates the development of efficient architectures for managing both historical and streaming data. The study analyzes levels of data temporality, their impact on database structures and computational performance, and the major limitations of traditional relational models under rapidly growing data loads. Based on an analysis of scientific publications and practical solutions, two promising approaches to temporal data organization are identified: a multidimensional MIVAR information space and the architecture of digital data factories. A comparative evaluation of these approaches is conducted, and a hybrid model combining the strengths of both directions is proposed, taking into account their respective advantages and constraints.

Keywords:digital twin; temporal data; data storage; stream processing; digital data factory; multidimensional models; MIVAR model; Big Data
 

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Citation link:
Andreev A. A. HYBRID MODEL FOR STORING AND PROCESSING TEMPORAL DATA IN DIGITAL TWIN SYSTEMS OF INDUSTRIAL OBJECTS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2026. -№02/2. -С. 30-35 DOI 10.37882/2223-2966.2026.02-2.03
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