Book Image

Architecting the Industrial Internet

By : Robert Stackowiak, Shyam Varan Nath, Carla Romano
Book Image

Architecting the Industrial Internet

By: Robert Stackowiak, Shyam Varan Nath, Carla Romano

Overview of this book

The Industrial Internet or the IIoT has gained a lot of traction. Many leading companies are driving this revolution by connecting smart edge devices to cloud-based analysis platforms and solving their business challenges in new ways. To ensure a smooth integration of such machines and devices, sound architecture strategies based on accepted principles, best practices, and lessons learned must be applied. This book begins by providing a bird's eye view of what the IIoT is and how the industrial revolution has evolved into embracing this technology. It then describes architectural approaches for success, gathering business requirements, and mapping requirements into functional solutions. In a later chapter, many other potential use cases are introduced including those in manufacturing and specific examples in predictive maintenance, asset tracking and handling, and environmental impact and abatement. The book concludes by exploring evolving technologies that will impact IIoT architecture in the future and discusses possible societal implications of the Industrial Internet and perceptions regarding these projects. By the end of this book, you will be better equipped to embrace the benefits of the burgeoning IIoT.
Table of Contents (19 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Analytics, machine learning, and analyst tools


Analytics and machine learning applications are deployed in the speed layer, the batch layer, or are sometimes pushed to field gateways at the edge. Before exploring the architecture components of the speed and batch layers and where these applications are deployed, we'll have a look at how these applications are created and other tools that the business analyst might use.

A process for advanced analytics creation

Recognizing the coming deluge of huge data volumes, over 200 international organizations met to discuss and define an open standard for the analysis of these massive data sets at the beginning of this century. This consortium created what became known as the Cross-Industry-Standard-Process for Data Mining (CRISP-DM).

Note

Data mining versus machine learning Data mining is an older term. It was originally differentiated from machine learning, in that, data mining referred to human-initiated modeling of data. Machine learning referred to...