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What is Data Virtualization

Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence
Agile data integration approach organizations use to gain more insight from their data.
Published in Chapter:
Big Data Analysis in IoT
Aqeel-ur Rehman (Hamdard University, Pakistan), Rafi Ullah (Hamdard University, Pakistan), and Faisal Abdullah (Hamdard University, Pakistan)
DOI: 10.4018/978-1-4666-8505-5.ch015
Abstract
In IoT, data management is a big problem due to the connectivity of billions of devices, objects, processes generating big data. Since the Things are not following any specific (common) standard, so analysis of such data becomes a big challenge. There is a need to elaborate about the characteristics of IoT based data to find out the available and applicable solutions. Such kind of study also directs to realize the need of new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates and formats it is getting a huge challenge to deal with such variety of data. As IoT is providing processing nodes in quantity in form of smart nodes, it is presenting itself a good platform for big data analysis. In this chapter, characteristics of big data and requirements for big data analysis are highlighted. Considering the big source of data generation as well as the plausible suitable platform of such huge data analysis, the associated challenges are also underlined.
Full Text Chapter Download: US $37.50 Add to Cart
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Integrated Data Architecture for Business
Data management approach that allows an application to retrieve and manipulate data without requiring technical details about the data.
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Integrated Data Architecture for Business
Data management approach that allows an application to retrieve and manipulate data without requiring technical details about the data.
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Citizen Data in Distributed Computing Environments: Privacy and Protection Mechanisms
Data virtualization provides a modern data layer that enables users to access, combine, transform, and deliver datasets with breakthrough speed and cost-effectiveness.
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