Data Analytics in Computer Networking

The Case for Exploratory Data Analysis

Stenio FernandesCarleton University / CIn-UFPE

March 2016

Outline

Data Analysis - backgroundEDA basicsApplied EDA (Examples: WiFi simulated data)Q&AReferences

Data Analytics - Background

Data Science Pipeline

• Analytic Data• Analytic Code• Documentation• Distribution

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Report Writing for Data Science in R, Roger D. Peng, 2016

1. Stating and refining the question

2. Exploring the data

3. Building formal statistical models

4. Interpreting the results

5. Communicating the results

Epicycle of Analysis

The Art of Data Science, A Guide for Anyone Who Works with Data, Roger D. Peng and Elizabeth Matsui, 2016

• summarize the measurements in a single data set without further interpretation

Descriptive

• Searching for discoveries, trends, correlations, or relationships between multiple variables to generate ideas or hypotheses

Exploratory

• quantifying whether an observed pattern will likely hold beyond the data set in hand

Inferential

• uses a subset of measurements (the features) to predict another measurement (the outcome)

Predictive

• what happens to one measurement if you make another measurement change

Causal

• changing one measurement always and exclusively leads to a specific, deterministic behavior in another

DeterministicThe Elements of Data Analytic Style, A guide for people who want to analyze data, Jeff Leek, 2015

EDA basics

Why use EDA - Summary

• Maximize insight into a data set• Uncover underlying structure• Extract important variables• Detect outliers and anomalies• Test underlying assumptions• Develop parsimonious models• Determine optimal factor

settings

NIS

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• Show comparisons• Show causality, mechanism,

explanation• Show multivariate data

• Integrate multiple modes of evidence

• Describe and document the evidence

• Content is king JH U

nive

rsity

Answer to initial questions

What is a typical value for a certain feature?

What is the uncertainty for a typical value of a feature?

What is a good distributional fit for a feature?

What is the percentile distribution?

Does modification on one variable have an effect another variable?

Does a factor have an effect on performance metrics?

What are the most important factors?

What is the best function for relating a response variable to other variables?

What are the best settings for factors (i.e. levels)?

Can we separate signal from noise?

Can we extract any structure from multivariate data?

Does the data have outliers?

EDA Graphs

Understand data properties Find patterns in data

Suggest modeling strategies

Debug analyses

Applied EDAUsing R/ggplot2

(mpg dataset) -> fake wifi dataset

Practical Steps

Before performing any measurements or simulation• Identify• Performance Metrics• Performance Factors and Levels

• Caution: sometimes you have to guess the ranges for the levels• Use an educated guess

Don’t run tons of simulations / experiments (As previously discussed)

Plot quick and dirty graphs• No need for titles, labels

Some examples of EDA Graphs - WiFi Data (simulated)

• “Vendor” - factor / levels: LinkSys, …• “Model“ – factor / Levels: GST200, …• "Users_Max_Rate“ - factor (background traffic) /

levels: 1.6, 1.8,…,7.0 Mbps• "Year“ – factor / Levels: 1999, 2008• "BER“ – factor / Levels: 4, 5, 6, and 8• "Type“ – factor (type of user) / Levels: 4, f, r• Rate – performance metric (Mbps)• Distance - factor (distance from the AP) / “Levels:

50,100m

Features (Observation Variables)

Q&A

References• NIST’s Handbook of Statistics Engineering (online)• Report Writing for Data Science in R, Roger D. Peng, 2016• The Art of Data Science, A Guide for Anyone Who Works with Data, Roger D.

Peng and Elizabeth Matsui, 2016• The Elements of Data Analytic Style, A guide for people who want to analyze

data, Jeff Leek, 2015