Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Analytics for the Internet of Things (IoT)

You're reading from   Analytics for the Internet of Things (IoT) Intelligent analytics for your intelligent devices

Arrow left icon
Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787120730
Length 378 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Andrew Minteer Andrew Minteer
Author Profile Icon Andrew Minteer
Andrew Minteer
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Defining IoT Analytics and Challenges 2. IoT Devices and Networking Protocols FREE CHAPTER 3. IoT Analytics for the Cloud 4. Creating an AWS Cloud Analytics Environment 5. Collecting All That Data - Strategies and Techniques 6. Getting to Know Your Data - Exploring IoT Data 7. Decorating Your Data - Adding External Datasets to Innovate 8. Communicating with Others - Visualization and Dashboarding 9. Applying Geospatial Analytics to IoT Data 10. Data Science for IoT Analytics 11. Strategies to Organize Data for Analytics 12. The Economics of IoT Analytics 13. Bringing It All Together

Solving industry-specific analysis problems


We will touch on a few industries to discuss special consideration for IoT data exploration and analysis.

Manufacturing

For IoT data generated during the manufacturing process, the accuracy of recorded values is especially important. Explore the data for outliers and analyze distributions carefully. Verify all the data ranges and distributions that you see with the experts on the manufacturing process.

The benefits of making sure the measurement values are clean as possible are two fold. First, any machine learning models created to detect problems will be significantly more accurate. Secondly, false positives due to invalid data can have a high penalty. The manufacturing line and product deliveries may be halted while the issue is investigated. In manufacturing, this can get expensive quickly. More perniciously, the long-term effect of false positives tends to be the complete rejection of the analytics by company management, when they no longer trust...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime