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doctype html
html
head
title The quest for insights, the true objective of big data
meta(charset='utf-8')
meta(name='author', content='John Alexis Guerra Gomez')
meta(name='description', content='The quest for insights, the true objective of big data')
meta(name="viewport",content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui")
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script.
var link = document.createElement( 'link' );
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document.getElementsByTagName( 'head' )[0].appendChild( link );
script(src='lib/js/d3.min.js')
script(src='lib/js/bumpChartPhotos.js')
body
.reveal
.slides
section#title
h1 The quest for insights,
h2 the true objective of big data
p
a(href='http://johnguerra.co/', target='_blank') John Alexis Guerra Gómez
br
a(href='http://twitter.com/duto_guerra') @duto_guerra
p.small Use
strong spacebar
| and the arrows to advance slides
p.small
a(href='http://infovis.co/bigDataQuest', target='_blank') http://infovis.co/bigDataQuest
//- p.small
//- a(href='http://www.cientificosdedatos.com', target='_blank') http://www.cientificosdedatos.com
section#outline
h2 Outline
ol
li Who am I?
li What is Big Data?
li How to process/store it?
li How to make sense of it?
//- ------------------------WHOAMI----------------------
section#whoamI
section
h1 Who am I?
section
//- h2 Ingeniero de sistemas y computación UTP
img(src="images/Pereira.jpg")
section#IRIS
img(src="images/IRIS_y_su_caja_peq.png")
section#KebSolutions
img(src="images/logo_keb.png")
section#Fulbright
img(src="images/fulbright.png")
section#Doctorado
h1 PhD
section#HCIL
img(src="images/Ben_and_Catherine.jpg")
section
iframe.blocks(src="https://cdn.rawgit.com/mbostock/4063582/raw/dd152a94f98ab7081a41096a88ece375e64a8b6c/index.html")
section
img(src="images/lifelines.jpg")
section
img(src="images/lifeflow.png")
section
img(src="images/2012_08_20_paxs_overview_september_01.png")
section
img(src="images/treeversity_v2.png")
section
//- img(src="images/2013_03_29_Budget_2013_Reporting2.png")
section
img(src="images/2013_04_29_eBay.png")
section
img(src="images/13CaseStudies.png")
section
h2 Silicon Valley
//- img(src="images/mapaCalifornia.png")
section
img(src="images/parc_rgb.png")
section
img.demo(src="images/topics_steamchart_with_wordclouds.png")
section
img(src="images/labs_us_purple_1000px_wide.png")
//- section
//- img(src="images/Flickr.png")
section
img(src="images/christmass.png")
section
img(src="images/FlickrPopular.png")
section
img(src="images/topPhotos.png")
section
img(src="images/photoRing.png")
//- section
//- img(src="images/GeoExplorer.png")
//- section
//- h2 Many other projects
//- section
//- img(src="images/TradeFlow.png")
//- section
//- img(src="images/Tweetometro.png")
//- section
//- img(src="images/Wholikesmyfb.png")
//- section
//- iframe.blocks(src="https://cdn.rawgit.com/john-guerra/4db5024e00a82f10a2dc/raw/1c7d368565d6c59dc9608dc5e792786616c26aff/index.html")
//- //- img(src="images/ForceColombia.png")
//- section
//- img(src="images/MapaColombia.png")
//- section
//- img(src="images/VIT.png")
//- section
//- img(src="images/QueCarroComprar.png")
//- ------------------WHAT'S BIG DATA----------------------
section#whatIsBigData
section
h1 What is Big Data?
section
h2 You might have heard of the Vs of Big Data
ul
li.fragment Volume
li.fragment Velocity
li.fragment Variety
li.fragment and Veracity and Value
li.fragment Too ambiguous!! Let's go beyond that
section
h2 How Big is big?
p.fragment Can you fit it in one computer?
p.fragment Yes? -> Then is not really big
p.fragment Let's call it big data only if it doesn't fit on one computer (and has the 3Vs)
section
h2 Why this criteria?
p.fragment Because if it fits in one computer you don't need all the overhead of big data technologies, just use a traditional relational database.
section
img(src="images/datafit1.svg")
section
img(src="images/datafit2.svg")
section
h2 Example: photo collection
ul
li.fragment One photo -> 10MB
li.fragment 1k photos in a cellphone -> 10MB * 1k = 10000MB = 10GB
li.fragment 50k photos in your computer -> 10MB * 50k = 500GB
li.fragment Is that big data?
li.fragment No, you can fit that in one cheap external hard drive
//- li.fragment BTW, Flickr gives you 1000GB (1TB) for free
section
h2 Problem: count how many blue photos in my collection?
p How do you compute this?
p.fragment Put all your photos in one computer
p.fragment Go through all the collection and count
section
img(src="images/datafit_processing.svg")
section
h2 Flickr size
p 80+ trillion photos (80'''000''000'000.000)
p.fragment That's big data
section
h2 How many blue photos on Flickr?
p How do you compute this?
p.fragment Distribute the data among hundreds of thousand of computers (a cluster).
p.fragment Compute subtotals on each chunk of the data. (Map)
p.fragment Aggregate the subtotals into one big total. (Reduce)
section
img(src="images/dont_fit.svg")
section
img(src="images/dont_fit2.svg")
section
img(src="images/dont_fit_blocks.svg")
section
img(src="images/dont_fit_blocks_distributed.svg")
section
img(src="images/map_reduce.svg")
section
h2 How many computers do you need?
p.fragment total / one computer capacity?
p.fragment What if one computer breaks down?
p.fragment We need redundancy -> Each photo is stored in many computers
p.fragment How do we control versions? How to keep records? What goes where?
p.fragment
strong That's why we need big data!!
section
img(src="images/redundancy.svg")
section#Technologies
h2 Technologies
ul
li.fragment MapReduce (Hadoop, Hive, pig, Spark ...)
li.fragment NoSQL Databases (Redis, Cassandra, MongoDB, Neo4J)
li.fragment Distributed Relational (SQL) Databases (MySQL, PostgreSQL, Oracle, SqlServer)
li.fragment Many others
section
h2 Hadoop
ul
li Computing platform for big data
li Uses clusters for storing and processing the data
section
h2 Hadoop Architecture
img(src="images/Hadoop Architecture.svg")
section
h2 Spark
p A distributed computing alternative of to map reduce.
ul
li Easier to use
li Integrates better with traditional programming models
section
h2 NoSQL Databases
ul
li Scalable storage platforms that use techniques different to traditional SQL databases
li Sacrifices features for performance
section
h2 Types of NoSQL
ul
//- #[a(href="https://en.wikipedia.org/wiki/Apache_Cassandra") Cassandra]
li.fragment Column Oriented: Cassandra, HBase, Redshift ...
li.fragment Key-value: Redis, memcached, Aerospike ....
li.fragment Document based: MongoDB, CouchDB, DynamoDB ...
li.fragment Graph based: Neo4J, Titan, ...
section
h2 Bonus
p #[a(href="https://vimeo.com/156305374") Introduction to NoSQL for Web Developers]
section
h2 Distributed Relational DB
ul
li You can also use traditional databases on a distributed way.
li Divides the database into shards.
li Usually doesn't scale that well.
section
h2 Others
ul
li Google #[a(href="https://cloud.google.com/dataflow/") DataFlow]
li Google's replacement for MapReduce based on flows.
li Supposed to scale better.
li AFAIK can only be used with Google's Cloud.
//- ------------------HOW TO MAKE SENSE----------------------
section#howToMakeSense
section
h1 Making sense
section
h2 How to make sense of it?
ul
li.fragment Statistical Analysis
li.fragment Machine Learning and Artificial Intelligence
li.fragment Visual Analytics (and data analytics)
section
h3 Data Mining/Machine Learning
p: img(src='images/machine_learning_diagram.png')
section
h3 Information Visualization
p: img(src='images/infovis_diagram.png')
section
h3 Infovis + Algorithms
p: img(src='images/infovis_algorithms_diagram.png')
section
table#comparisonTable
tr
td.fragment
h2 Traditional
ul
li Query for known patterns
li Display results using traditional techniques
br
strong Pros:
br
ul
li Many solutions
li Easier to implement
br
strong Cons:
br
ul
li Can’t search for the unexpected
td.fragment
h2 Data Mining/ML
ul
li Based on statistics
li Black box approach
li Output outliers and correlations
li Human out of the loop
br
strong Pros:
br
ul
li Scalable
br
strong Cons:
ul
li Analysts have to make sense of the results
li Makes assumptions on the data
td.fragment
h2 InfoVis
ul
li Visual Interactive Interfaces
li Human in the loop
br
strong Pros:
br
ul
li Visual bandwidth is enormous
li Experts decided what to search for
li Identify unknown patterns and errors in the data
br
strong Cons
br
ul
li Scalability can be an issue
section
h2 Why should we visualize?
section
h2 Anscombe's quartet
img(src="images/anscombes.jpg")
section
h2 Anscombe's quartet
img(src="images/anscombes2.jpg")
section
h2 Anscombe's visualized
img(src="images/anscombes_graph.jpg")
section
h2 In Infovis we look for #[strong Insights]
ul
li.fragment Deep understanding
li.fragment Meaningful
li.fragment Non obvious
li.fragment Actionable
section
h2 How do I do it?
img.demo(src="images/My skills.png")
section#BuscandoInsights
h2 What do I use?
img.demo(src="images/What I use.png")
section
section
h1 Insights
section#InsightFDA
h2 FDA
p Task: Change in drug's adverse effects reports
p User: FDA Analysts
section
h2 State of the art
img.demo(src="images/sectorMap_drug1.png")
section
img.demo(src="images/sectorMaps.png")
section
img(src="images/skylines_explanation.png")
section
a(href="https://treeversity.cattlab.umd.edu/?hierarchy=soc/hlgt/hlt/pt&db=tv2_sectorMaps6&seqFrom=01/01/08&seqTo=01/01/12&viz=Skylines&fixedHierarchy=soc/hlgt/hlt/pt&valueAttrib=ebgm&title=FDA%20Adverse%20Drug%20Effects" target="_blank")
img(src="images/2013_03_29_FDA_all.png")
a(href="https://treeversity.cattlab.umd.edu/") https://treeversity.cattlab.umd.edu/
section#InsightPARC
h2 Health insurance claims
p Task: Detect fraud networks
p User: Undisclosed Analysts
section
img.demo(src="images/network_explorer_before.png")
section
h2 Clustering
//- img(src="images/.jpg")
iframe.blocks(src="https://cdn.rawgit.com/john-guerra/ecdde32ab4ad91a1a240/raw/2c1a843df8631604a99140ddc9db8ee048624a79/index.html")
section
h2 Force in a box
iframe.blocks(src="https://cdn.rawgit.com/john-guerra/14c943d8f198d9f3fef2/raw/320474d468321d00f3241609a125c8d37935474b/index.html")
section
h2 Overview
img.demo(src="images/overview_10k_nointer_cropped.png")
section
h2 Ego distance
img.demo(src="images/network_explorer_ego_distance.png")
section#InsightTweetometro
h2 Tweetometro
p Task: Twitter behavior during Presidential Elections
p User: Me
section
iframe.blocks(src="http://tweetometro.co" alt="images/tweetometro.png")
a(href="http://tweetometro.co") http://tweetometro.co
section
h2 Normal tweets
img.demo(src="http://tweetometro.co/img/all_tweets_May_25_2014.png")
section
h2 Weird tweets?
img.demo(src="http://tweetometro.co/img/suspicious_tweets_May_25_2014.png")
section
h2 Creation dates
img.demo(src="http://tweetometro.co/img/user_creation_date_May_25_2014.png")
section
h2 Number of followers
img.demo(src="http://tweetometro.co/img/num_followers_May_25_2014.png")
section
span.
<script type='text/javascript' src='http://public.tableau.com/javascripts/api/viz_v1.js'></script><div class='tableauPlaceholder' style='width: 1032px; height: 677px;'><noscript><a href='http://tweetometro.co/robots_May25.html'><img alt='Análisis Elecciones Presidenciales Colombia ' src='http://public.tableau.com/static/images/An/AnlisisPosiblesRobotsEleccionesColombiaMay25/AnlisisEleccionesPresidencialesColombia/1_rss.png' style='border: none' /></a></noscript><object class='tableauViz' width='1032' height='677' style='display:none;'><param name='host_url' value='http%3A%2F%2Fpublic.tableau.com%2F' /> <param name='site_root' value='' /><param name='name' value='AnlisisPosiblesRobotsEleccionesColombiaMay25/AnlisisEleccionesPresidencialesColombia' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='http://public.tableau.com/static/images/An/AnlisisPosiblesRobotsEleccionesColombiaMay25/AnlisisEleccionesPresidencialesColombia/1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /></object></div>
section#InsightCars
h2 What car to buy?
p Task: What's the best car to buy?
p User: Me
section
h2 Normal procedure
p Ask friends and family
section#Renault4
span.
<a data-flickr-embed="true" href="https://www.flickr.com/photos/synx508/8083533370/in/photolist-djjdR1-afaxQE-88rrWV-85GG7C-dhdprm-cB1GP1-6T5Y57-bwHLJo-kCowWc-q7ohL2-pZdWRQ-aqbuDd-CwU436-8Eruxo-nzoa7c-BzobPh-ptWmt6-BYoC9H-81RCdE-dPuGGx-q4JtAk-7XXciZ-r1UjFV-iKrUr5-iEmCiG-pcraD8-fNGpSf-fNGpFd-cN7xp-7SC3f9-D3mfHj-dhdpZ8-nzh5GD-85GGwL-5wBWcW-7tCm5D-8RDcp9-7kBmch-afaxQJ-8RA446-9z4AuX-6B9YeE-9ZVygr-7SC3f5-e3sALe-6B5Zon-apFGuQ-a6gMVY-5wVRyj-7pJkMr" title="Renault 4"><img src="https://c3.staticflickr.com/9/8055/8083533370_a0597b2b89_b.jpg" width="1024" height="683" alt="Renault 4"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>
section#Renault4Pimped
span.
<a data-flickr-embed="true" href="https://www.flickr.com/photos/zenzak35/14225528098/" title="Renault 4 JP4"><img src="https://c3.staticflickr.com/4/3909/14225528098_f77d8393ac_b.jpg" width="1024" height="576" alt="Renault 4 JP4"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>
section#Renault4Crashed
span.
<a data-flickr-embed="true" href="https://www.flickr.com/photos/95012335@N02/16327150369/in/photolist-qSLUYz-p8zsEJ-fHL1jE-4grA8b-8YBfrt-8mRG7n-49vZ6h-dXiuZG-kseXjY-CpB1rB-kCp4NF-75n5c9-8PiBKZ-5999UC-C5Lz5Y-eBWbNx-eBEvp-fBpDP7-nF4xTQ-8spzYg-a7ovQd-4r1u5g-aPE45k-7R62M3-dZQnYp-djjdR1-afaxQE-88rrWV-85GG7C-dhdprm-cB1GP1-6T5Y57-bwHLJo-kCowWc-q7ohL2-pZdWRQ-aqbuDd-CwU436-8Eruxo-nzoa7c-BzobPh-ptWmt6-BYoC9H-81RCdE-dPuGGx-q4JtAk-7XXciZ-r1UjFV-iKrUr5-iEmCiG" title="Teilgefalteter Renault 4 am Strassenrand"><img src="https://c2.staticflickr.com/8/7437/16327150369_c5a839efab_b.jpg" width="1024" height="681" alt="Teilgefalteter Renault 4 am Strassenrand"></a><script async src="//embedr.flickr.com/assets/client-code.js" charset="utf-8"></script>
section
h2 Problem
p That's inferring statistics from a sample n=1
section
h2 Better approach
p Data based decisions
section
img(src="images/tucarro.png")
a(href="http://tucarro.com") http://tucarro.com
section
iframe.blocks(src="http://infovis.co/carrosUsados/todosPuntos.html")
section
iframe.blocks(src="http://infovis.co/carrosUsados/depreciacionMarcas.html")
section
iframe.blocks(src="http://infovis.co/carrosUsados/depreciacionesCarros.html")
//- section
//- h1 Visual Analytics en Wingz
//- section
//- img.demo(src="images/PriceDeviation.png")
//- p Insight: Estamos comprando a precios no competitivos
//- section
//- img.demo(src="images/Venta media la 94.png")
//- p Insight: Generalmente el domingo se vende más, en todos menos en el restaurante de la 94
//- section
//- img(src="images/vendedores_robando_blurred.png")
//- p Insight: Tenemos vendedores tomando aprovechándo las falencias del sistema
//- section
//- img(src="images/vendedores_robando2_blurred.png")
//- p Insight: Tenemos vendedores tomando aprovechándo las falencias del sistema
//- section
//- img(src="images/clientes.png")
//- p Insight: Tenemos clientes VIP desatendidos
//- section
//- img(src="images/ventas_por_hora_dia.png")
//- p Insight: No todas las horas del día son iguales
//- section
//- img(src="images/vendedores_productos.png")
//- p Analítica: ¿Qué vendedores venden más ciertos productos?
//- section
//- img(src="images/clientes.png")
//- p Analítica: ¿Cuáles son los productos favoritos de nuestros clientes?
section
h2 Take home message
ul
li.fragment Big data? Sure, If it doesn't fit on a computer
li.fragment Finding #[strong insights], that's what matters
li.fragment #[strong Visual Analytics], a good way of finding insights
section#end
h1 Thank You
h2 Questions?
div.contactInfo
p John Alexis Guerra Gómez
a(href="http://johnguerra.co") johnguerra.co
br
a(href="http://twitter.com/duto_guerra") @duto_guerra
section#Bonus
h1 Bonus
//- --------------------- Types of visualization
section#typesOfVisualization
section
h2 Types of Visualization
ul
li Infographics
li Scientific Visualization (sciviz)
li Information Visualization (infovis, datavis)
section
h3 Infographics
img(src="images/infographics.png")
section
h3 Scientific Visualization
ul
li Inherently spatial
li 2D and 3D
p: img(src="images/sciviz.png")
section
h3 Information Visualization
img(src="images/infovis_examples.png")
section
h2 Infovis Basics
section
h2 Visualization Mantra
ul
li Overview first
li Zoom and Filter
li Details on Demand
section#munznerstyle
iframe(width='100%', height='500px', scrolling='no', frameborder='no', src= "munzner.html")
section#munznerpreference
h2 Perception Preference
div#munznerpreferencechart
script.
myBumpChart = bumpChartPhotos()
.x(function (d) { return d.dataType; })
.y(function (d) { return d.position; })
.key(function (d) { return d.attribute; })
.label(function (d) { return d.attribute; })
.width(900)
.height(400);
//- img(src="images/munzner_preference.png", height="400px")
d3.json("munzner_preference.json", function (err, data) {
var procData =[];
data.forEach(function (c) {
c.preference.forEach(function (p, i) {
procData.push({
"dataType":c.type,
"position":i,
"attribute":p
});
});
});
d3.select("#munznerpreferencechart")
.datum(procData)
// .style("height", timelineHeight + "px")
.call(myBumpChart);
});
p.small Adapted from from:
a(href="http://www.cs.ubc.ca/labs/imager/tr/2009/VisChapter/akp-vischapter.pdf", target='_blank') Tamara Munzner Book Chapter
section#dataTypes
section
h2 Data Types
table.small
tr
td: strong 1-D Linear
td Document Lens, SeeSoft, Info Mural
tr
td: strong 2-D Map
td GIS, ArcView, PageMaker, Medical imagery
tr
td: strong 3-D World
td CAD, Medical, Molecules, Architecture
tr
td: strong Multi-Var
td Spotfire, Tableau, GGobi, TableLens, ParCoords,
tr
td: strong Temporal
td LifeLines, TimeSearcher, Palantir, DataMontage, LifeFlow
tr
td: strong Tree
td Cone/Cam/Hyperbolic, SpaceTree, Treemap, Treeversity
tr
td: strong Network
td Gephi, NodeXL, Sigmajs
//- //- ----------- Cómo lo hago
//- section
//- section
//- h2 Traditional Data Science
//- img.demo(src="images/Traditional Data Science.png")
//- section
//- h2 Visual Analytics
//- img.demo(src="images/Visual Analytics.png")
script(src='lib/js/head.min.js')
script(src='lib/js/reveal.js')
script.
Reveal.initialize({
controls: true,
progress: true,
history: true,
center: true,
rollingLinks: true,
transition: "convex",
//- width: "90%",
//- height: 1.0,
dependencies: [
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{ src: 'lib/js/classList.js', condition: function() { return !document.body.classList; } },
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{ src: 'plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
// Syntax highlight for <code> elements
{ src: 'plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } },
// Zoom in and out with Alt+click
{ src: 'plugin/zoom-js/zoom.js', async: true },
// Speaker notes
{ src: 'plugin/notes/notes.js', async: true },
//- // Remote control your reveal.js presentation using a touch device
//- { src: 'plugin/remotes/remotes.js', async: true },
//- // MathJax
//- { src: 'plugin/math/math.js', async: true }
]
});
script.
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ga('create', 'UA-50178794-5', 'auto');
ga('send', 'pageview');