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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining 2. Fuzzy Logic Induced Content-Based Recommendation FREE CHAPTER 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Streaming data and its challenges


Streaming data poses infrastructural and processing challenges. Major tech companies are inventing new data structures and server mechanisms to handle the huge volume and velocity of the streaming data. Software infrastructures such as Kafka, Storm, Bolt, and other similar technologies are being invented to handle this from an infrastructure perspective. We will not go into the details here. Our concern is primarily with the processing challenges.

The processing challenges in stream data are shown in the following figure:

Bounded problems

The first challenge is deciding on a window, and what size window we need to accommodate to make sense of the incoming data. By window, we mean storing the last n data points. For streaming data, it is rare to actually process records one at a time. One of the most common ways to process streaming data is to process them in a window. This can either bundle data points into a group and process them as a unit or it can be a...

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