Book Image

Analytics for the Internet of Things (IoT)

By : Andrew Minteer
5 (1)
Book Image

Analytics for the Internet of Things (IoT)

5 (1)
By: Andrew Minteer

Overview of this book

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Raster-based methods


Raster consists of a grid of cells arranged in rows and columns. Think of raster like pixels on a screen, except each pixel is defined using a set ground distance. There is a lot in common between raster files and image files. Raster files are sometimes saved using the same formats as image files. Images are often created straight from raster files; you see such examples all the time, from weather forecasts to terrain maps.

The size of the cells in the grid is similar in concept to the resolution of an image. Unlike vector data, a raster contains information for the entire area it covers. It is useful for things that have values for an entire area, such as elevation and temperature. The downside is the resulting large file sizes.

Multiple values per cell can be stored as different bands in the dataset. This is similar in concept to RGB values for a color image. The SRTM and Digital Elevation Model (DEM) datasets discussed in Chapter 7, Decorating Your Data - Adding External...