Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

Trends and Technologies in Big Data Processing

2021 ◽  
pp. 42-67
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

scholarly journals Fog Computing Mitigate Limitations of Cloud Computing

2020 ◽  
Vol 8 (5) ◽  
pp. 1732-1736
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Public Cloud ◽  
Time Data ◽  
Security Issues ◽  
Computing Model ◽  
Real Time Data ◽  
Real Time Data Processing

In today’s world, cloud computing is the most exciting and advanced technology. It came into existence with lots of advantages, but cloud-only computing has some disadvantages also like latency in real-time data processing, network congestion, less bandwidth utilization, fault tolerance, and security issues in public cloud. To address the issue of real-time data-processing and security in public cloud new computing model are used which is known as Fog Computing. It is nearer to the client or edge so that it can reduce the latency in real time data-processing and security in public cloud using techniques like user profiling and decoying technique. Fog Computing help us to overcome the latency and security issues of cloud computing. It reduces cloud latency in real time data-processing because fog computing model is nearer to the edge devices. It also improves cloud security in the public cloud.

scholarly journals Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bhagya Nathali Silva ◽  
Murad Khan ◽  
Kijun Han
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.

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