Predictive analysis visualization component in simulated data streams

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Dades bibliogràfiques
Publicat a:	Information Retrieval vol. 27, no. 1 (Dec 2024), p. 12
Publicat:	Springer Nature B.V.
Matèries:	Big Data Machine learning Simulation Software Datasets User behavior Computer science Data sources Sensors Data systems Data processing Design Data analysis Data collection Data transmission Visualization Case studies
Accés en línia:	Citation/Abstract Full Text - PDF
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Descripció
Resum:	One of the most significant problems related to Big Data is their analysis with the use of various methods from the area of descriptive statistics or machine and deep learning. This process is interesting in both—static datasets containing various data sources which do not change over time, and dynamic datasets collected with the use of ambient data sources, which measure a number of attribute values over long periods. Since access to actual dynamic data systems is demanding, the focus of this work is put on the design and implementation of a framework usable in a simulation of data streams, their processing and subsequent dynamic predictive and visual analysis. The proposed system is experimentally verified in the context of a case study conducted on an environmental variable dataset, which was measured with the use of a real-life sensor network.
ISSN:	1386-4564 1573-7659
DOI:	10.1007/s10791-024-09447-4
Font:	ABI/INFORM Global