FoT-Stream: A Fog platform for data stream analytics in IoT

2020 ◽  
Vol 164 ◽  
pp. 77-87
Author(s):  
Brenno M. Alencar ◽  
Ricardo A. Rios ◽  
Cleber Santana ◽  
Cássio Prazeres
Keyword(s):  
Data Stream ◽  
Stream Analytics

Clouds Proportionate Medical Data Stream Analytics for Internet of Things-based Healthcare Systems

2021 ◽  
pp. 1-1
Author(s):  
Priyan Malarvizhi KUMAR ◽  
Choong Seon Hong ◽  
Fatemeh Afghah ◽  
Gunasekaran Manogaran ◽  
Keping Yu ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Stream ◽  
Healthcare Systems ◽  
Medical Data ◽  
Stream Analytics

scholarly journals Multi-Objective Reinforcement Learning for Reconfiguring Data Stream Analytics on Edge Computing

2019 ◽  
Author(s):  
Alexandre da Silva Veith ◽  
Felipe Rodrigo de Souza ◽  
Marcos Dias de Assunção ◽  
Laurent Lefèvre ◽  
Julio Cesar Santos dos Anjos
Keyword(s):  
Reinforcement Learning ◽  
Data Stream ◽  
Edge Computing ◽  
Multi Objective ◽  
Stream Analytics

scholarly journals Evaluative Review of Streaming Analytics: Tools and Technologies in Real-Time Data Processing

2021 ◽  
pp. 423-432
Author(s):  
Ms. Shailaja B. Jadhav ◽  
Dr. D. V. Kodavade
Keyword(s):  
Data Processing ◽  
Data Stream ◽  
Research Field ◽  
Streaming Data ◽  
Rail Transportation ◽  
Time Data ◽  
Data Record ◽  
Real Time Data Processing ◽  
Stream Analytics

Nowadays, big data processing systems are evolving to be more stream-oriented; where each data record is processed as it arrives by distributed and low latency computational frameworks [18]. Data streams have been extensively used in several fields of computational analytics such as data mining, business intelligence etc. [17]. In every field, the data stream can be considered as an ordered sequence of data items, as they continuously arrive over the period. Due to this characteristic, streaming data analytics is a challenging area of research [5, 11]. This paper aims to present data stream processing as a growing research field , along with streaming analytics frameworks as a rich focus area. The paper also contributes to evaluate the efficacy of available stream analytics frameworks. One of the Industry 4.0 use case - predictive maintenance rail transportation - has been illustrated here as a case study design mapped with streaming analytics framework.

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