A Reference Architecture for Supporting Secure Big Data Analytics over Cloud-Enabled Relational Databases

2016 ◽  
Author(s):  
Alfredo Cuzzocrea
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Relational Databases ◽  
Big Data Analytics ◽  
Reference Architecture

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.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

2020 ◽  
Vol 15 (2) ◽  
pp. 114-119 ◽  
Author(s):  
Nicolas Zhou ◽  
Erin M. Corsini ◽  
Shida Jin ◽  
Gregory R. Barbosa ◽  
Trey Kell ◽  
...  
Keyword(s):  
Big Data ◽  
Clinical Research ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Data Analytics ◽  
Relational Databases ◽  
Big Data Analytics ◽  
Rapid Expansion ◽  
Data Infrastructure

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

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.

scholarly journals Comparison between SQL and NoSQL Databases and Their Relationship with Big Data Analytics

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Wajid Ali ◽  
Muhammad Usman Shafique ◽  
Muhammad Arslan Majeed ◽  
Ali Raza
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Relational Databases ◽  
Web Applications ◽  
Distributed Storage ◽  
Big Data Analytics ◽  
Nosql Databases ◽  
Advantages And Disadvantages ◽  
The World

A key ingredient in the world of cloud computing is a database that can be used by a great number of users. Distributed storage mechanisms become the de-facto method for data storage used by companies for the new generation of web applications. In the world of data storage, NoSQL (usually interpreted as "not only SQL" by developers) database is a growing trend. It is said that NoSQL alternates with the most widely used relational databases for the data storage, but, as the name implies, it does not fully replace the SQL. In this paper we will discuss about SQL and NoSQL databases, comparison of traditional SQL with NoSQL databases for Big Data analytics, NoSQL data models, types of NoSQL data stores, characteristics and features of each data store, advantages and disadvantages of NoSQL and RDBMS.

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