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001-es BibID:	BIBFORM125415
Első szerző:	Gál Zoltán (informatikus)
Cím:	Identification of Education Activity Based on Datalake Captured from Internal Sensor Data of Supercomputer / Zoltan Gal
Dátum:	2024
Megjegyzések:	Analysis of the nonlinear and nonstationary systems requires special methods, different from the classical Fourier Transform. Fast Fourier Transform, Wavelets, and Wavelet Transform let researchers find temporal properties of the data series in different frequencies, time moments and scales. Partitioning solutions of a single-dimensional nonstationary signal with Empirical Mode Decomposition or Variational Mode Decomposition creates a set of intrinsic mode functions, representing each of them as a stationary component in specific frequency bands. Both decompositions identify important characteristic features called modes of the initial process. These modes being stationary can be analysed with the Fourier Transform and applied classical statistical methods of the time series. Dynamic Mode Decomposition is proposed for the analysis of coherent multidimensional processes distributed in space and time simultaneously. Eigenvalues, eigenvectors and modes help to identify spatiotemporal structures of the nonstationary initial process. It is nontrivial to evaluate the scientific effect of the joint usage of these different methods. In this paper, the analysis will be executed on the multidimensional dataset captured from inside a supercomputer machine with 127 computation nodes organised in three different operation queues and used for specific education and research activity. The dataset with 1.7 million samples was uploaded into the Kaggle database. Internal data from physical sensors are the temperature of memory cards, processors, and systems. Internal data from logical sensors are the load of the node, number of running processes, number of input and output packets on the network interface card, and load of the processors. The sampling period and duration of these data series were one minute and one week, respectively. It was found that the joint usage of the specific methods depends on the character of the dataset.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok könyvfejezet könyvrészlet supercomputer education big data datalake time series analysis Fourier Transform
Megjelenés:	2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI) / Teen-Hang Meen. - p. 217-223
Pályázati támogatás:	TKP2021-NKTA-34 Egyéb
Internet cím:	DOI Intézményi repozitóriumban (DEA) tárolt változat
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