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THE IMPACT OF SYNTHETIC STREAMING DATA GENERATED BY GENERATIVE MACHINE LEARNING MODELS ON ANALYTICS AND EARLY DETECTION METHODS

Ulanov Kirill Anatolyevich  (Postgraduate Student, Department of Information Systems Moscow State Technological University "Stankin" )

The article explores how synthetic streaming data generated by modern generative models affects the operation of real-time analytical services and deviation detection systems. A formal intervention model with proportion–intensity–diversity parameters is proposed for the data flow processing stack. Experiments on real synthetic data streams have shown that as many as 5% of synthetic messages can significantly impair the accuracy of forecasts and increase the delay in detecting anomalies. The work contributes to the theory of data quality control and formulates practical recommendations on the use of synthetic streams.

Keywords:generative streaming data, synthetic data, deviation detection, data quality control, streaming analytics.
 

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Citation link:
Ulanov K. A. THE IMPACT OF SYNTHETIC STREAMING DATA GENERATED BY GENERATIVE MACHINE LEARNING MODELS ON ANALYTICS AND EARLY DETECTION METHODS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2025. -№06/2. -С. 221-223 DOI 10.37882/2223-2966.2025.06-2.41
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