Abstract
The rapid development of digital technologies, IoT products and connectivity platforms, social networking applications, video, audio and geolocation services has created opportunities for collecting/accumulating a large amount of data. While in the past corporations used to deal with static, centrally stored data collected from various sources, with the birth of the web and cloud services, cloud computing is rapidly overtaking the traditional in-house system as a reliable, scalable and cost-effective IT solution. The high volumes of structures and unstructured data, stored in a distributed manner, and the wide variety of data sources pose problems related to data/knowledge representation and integration, data querying, business analysis and knowledge discovery. This introductory chapter serves to characterize the relevant aspects of the Big Data Ecosystem with respect to big data characteristics, the components needed for implementing end-to-end big data processing and the need for using semantics for improving the data management, integration, processing, and analytical tasks.
Highlights
In 2001, in an attempt to characterize and visualize the changes that are likely to emerge in the future, Douglas Laney [271] of META Group (Gartner ) proposed three dimensions that characterize the challenges and opportunities of increasingly large data: Volume, Velocity, and Variety, known as the 3 Vs of big data
According to Manyika et al [297] this definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data
Regardless of how many descriptors are isolated when describing the nature of big data, it is abundantly clear that the nature of big data is highly complex and that it, as such, requires special technical solutions for every step in the data workflow
Summary
In 2001, in an attempt to characterize and visualize the changes that are likely to emerge in the future, Douglas Laney [271] of META Group (Gartner ) proposed three dimensions that characterize the challenges and opportunities of increasingly large data: Volume, Velocity, and Variety, known as the 3 Vs of big data. According to Manyika et al [297] this definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data. Along this lines, big data to Amazon or Google (see Table 1) is quite different from big data to a medium-sized insurance or telecommunications organization. Additional “Vs” of data have been added over the years, but Volume, Velocity and Variety are the tree main dimensions that characterize the data. Regardless of how many descriptors are isolated when describing the nature of big data, it is abundantly clear that the nature of big data is highly complex and that it, as such, requires special technical solutions for every step in the data workflow
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