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Artificial Intelligence for IoT Cookbook

You're reading from  Artificial Intelligence for IoT Cookbook

Product type Book
Published in Mar 2021
Publisher Packt
ISBN-13 9781838981983
Pages 260 pages
Edition 1st Edition
Languages
Author (1):
Michael Roshak Michael Roshak
Profile icon Michael Roshak

Table of Contents (11) Chapters

Preface 1. Setting Up the IoT and AI Environment 2. Handling Data 3. Machine Learning for IoT 4. Deep Learning for Predictive Maintenance 5. Anomaly Detection 6. Computer Vision 7. NLP and Bots for Self-Ordering Kiosks 8. Optimizing with Microcontrollers and Pipelines 9. Deploying to the Edge 10. About Packt

Tumbling

Tumbling window functions group data streams into time segments (as shown in the following diagram). Tumbling windows means that the window does not repeat or overlap data from one segment waterfall into the next:

Stream Analytics

In Stream Analytics, one way to use a tumbling window to count the events that happen every 10 seconds would be to do the following:

SELECT EventTime, Count(*) AS Count
FROM DeviceStream TIMESTAMP BY CreatedAt
GROUP by EventTime, TumbelingWindow(minuites, 10)

Spark 

In Spark, to do the same count of events happening every 10 minutes, you would do the following:

from pyspark.sql.functions import * 
windowedDF
= eventsDF.groupBy(window("eventTime", "10 minute")).count()
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Artificial Intelligence for IoT Cookbook
Published in: Mar 2021 Publisher: Packt ISBN-13: 9781838981983
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