Smart Transportation: A Reference Architecture for Big Data Analytics

2020 ◽  
pp. 161-179
Author(s):  
Camilo Castellanos ◽  
Boris Perez ◽  
Dario Correal
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Reference Architecture ◽  
Smart Transportation

Big-Data-Based Architectures and Techniques

2019 ◽  
pp. 19-50
Author(s):  
Gopala Krishna Behara
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Reference Architecture ◽  
The Real ◽  
Key Concepts ◽  
Insight Into

This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.

IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

Big-Data-Based Architectures and Techniques

2022 ◽  
pp. 197-227
Author(s):  
Gopala Krishna Behara
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Reference Architecture ◽  
The Real ◽  
Key Concepts ◽  
Insight Into

This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.

IoT Big Data Architectures, Approaches, and Challenges

2019 ◽  
pp. 125-148 ◽  
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

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