ABSTRACT
As explained in the beginning of this book, the overwhelming leverage of miniaturization, digitization, distribution, consumerization (mobility), consolidation, centralization and industrialization (cloud), compartmentalization (virtualization and containerization), and deeper connectivity technologies has a number of trendsetting and transformational implications on information technology (IT) organizations and business houses/behemoths across the globe. Edge or fog computing through cloudlets and microclouds is another potential phenomenon for next-generation IT. There will be a cool convergence in forming and firming up hyperconverged cloud environments to host and deliver smarter and sophisticated applications for the total humanity.
All these advancements are bound to bring forth a number of distinct outputs and opportunities. The principal one among them is the enormous growth in data size and the greater variability and variety in data scope, structure, and speed. That is, with the continuous growth of newer data sources and resources, the amount of data getting generated, captured, transmitted, and stored is tremendously huge. As data are turning out to be a strategic asset for any organization to be decisive, distinctive, and disciplined in its operations, offerings, and outputs, a host of competent technologies, tips, and tools are being unearthed to smartly stock and subject all incoming and stored data to a variety of deeper investigations to gain actionable insights in time.
Especially extracting and extrapolating knowledge out of data heaps in time go a long way in empowering every kind of enterprises and endeavors to be exceptionally efficient and effective in their deals, deeds, and deliveries. In this chapter, we would like to dig deeper and dwell at length on the various analytical approaches, frameworks, algorithms, platforms, engines, and methods for squeezing out value-adding and venerable insights out of Internet of Things (IoT) data.
