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Analytics for the Internet of Things (IoT)

You're reading from  Analytics for the Internet of Things (IoT)

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781787120730
Pages 378 pages
Edition 1st Edition
Languages
Author (1):
Andrew Minteer Andrew Minteer
Profile icon Andrew Minteer

Table of Contents (20) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Defining IoT Analytics and Challenges IoT Devices and Networking Protocols IoT Analytics for the Cloud Creating an AWS Cloud Analytics Environment Collecting All That Data - Strategies and Techniques Getting to Know Your Data - Exploring IoT Data Decorating Your Data - Adding External Datasets to Innovate Communicating with Others - Visualization and Dashboarding Applying Geospatial Analytics to IoT Data Data Science for IoT Analytics Strategies to Organize Data for Analytics The Economics of IoT Analytics Bringing It All Together

Creating and visualizing alerts


IoT data is inherently noisy. There are often cases of invalid and missing values. This requires constant vigilance to identify and correct data issues when they occur. The correction could then be handled in the transformation of the raw data or in the software and design of the device.

Either way, the faster an issue is detected, the quicker it can be resolved. For IoT data, consider bad data as lost money that can rarely be recovered. Minimize the loss by identifying and correcting issues quickly.

Dashboards can also be created for this purpose by following the same process we introduced in this chapter. Think about what you want to watch out for and set up an alert view to identify it for you.

Alert principles

There are some principles to follow when designing an alert system, even a simple one that will be part of a dashboard:

  • Balance alert sensitivity to minimize false positives: People will learn quickly to ignore alerts if they rarely identify an actual...
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