Predictive analysis visualization component in simulated data streams

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Publicado en:Information Retrieval vol. 27, no. 1 (Dec 2024), p. 12
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Springer Nature B.V.
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Acceso en línea:Citation/Abstract
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245 1 |a Predictive analysis visualization component in simulated data streams 
260 |b Springer Nature B.V.  |c Dec 2024 
513 |a Journal Article 
520 3 |a 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. 
653 |a Big Data 
653 |a Machine learning 
653 |a Simulation 
653 |a Software 
653 |a Datasets 
653 |a User behavior 
653 |a Computer science 
653 |a Data sources 
653 |a Sensors 
653 |a Data systems 
653 |a Data processing 
653 |a Design 
653 |a Data analysis 
653 |a Data collection 
653 |a Data transmission 
653 |a Visualization 
653 |a Case studies 
773 0 |t Information Retrieval  |g vol. 27, no. 1 (Dec 2024), p. 12 
786 0 |d ProQuest  |t ABI/INFORM Global 
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