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<!DOCTYPE html>
<html>
<head>
<title>The quest for insights, the true objective of big data</title>
<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">
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</head>
<body>
<div class="reveal">
<div class="slides">
<section id="title">
<h1>The quest for insights,</h1>
<h2>the true objective of big data</h2>
<p><a href="http://johnguerra.co/" target="_blank">John Alexis Guerra Gómez</a><br><a href="http://twitter.com/duto_guerra">@duto_guerra</a></p>
<p class="small">Use<strong> spacebar</strong> and the arrows to advance slides</p>
<p class="small"><a href="http://infovis.co/bigDataQuest" target="_blank">http://infovis.co/bigDataQuest</a></p>
</section>
<section id="outline">
<h2>Outline</h2>
<ol>
<li>Who am I?</li>
<li>What is Big Data?</li>
<li>How to process/store it?</li>
<li>How to make sense of it?</li>
</ol>
</section>
<section id="whoamI">
<section>
<h1>Who am I?</h1>
</section>
<section><img src="images/Pereira.png">
</section>
<section id="IRIS"><img src="images/IRIS_y_su_caja_peq.png"></section>
<section id="KebSolutions"><img src="images/logo_keb.png"></section>
<section id="Fulbright"><img src="images/fulbright.png"></section>
<section id="Doctorado">
<h1>PhD</h1>
</section>
<section id="HCIL"><img src="images/Ben_and_Catherine.jpg"></section>
<section>
<iframe src="https://cdn.rawgit.com/mbostock/4063582/raw/dd152a94f98ab7081a41096a88ece375e64a8b6c/index.html" class="blocks"></iframe>
</section>
<section><img src="images/lifelines.jpg"></section>
<section><img src="images/lifeflow.png"></section>
<section><img src="images/2012_08_20_paxs_overview_september_01.png"></section>
<section><img src="images/treeversity_v2.png"></section>
<section>
</section>
<section><img src="images/2013_04_29_eBay.png"></section>
<section><img src="images/13CaseStudies.png"></section>
<section>
<h2>Silicon Valley</h2>
</section>
<section><img src="images/parc_rgb.png"></section>
<section><img src="images/topics_steamchart_with_wordclouds.png" class="demo"></section>
<section><img src="images/labs_us_purple_1000px_wide.png"></section>
<section><img src="images/christmass.png"></section>
<section><img src="images/FlickrPopular.png"></section>
<section><img src="images/topPhotos.png"></section>
<section><img src="images/photoRing.png"></section>
</section>
<section id="whatIsBigData">
<section>
<h1>What is Big Data?</h1>
</section>
<section>
<h2>You might have heard of the Vs of Big Data</h2>
<ul>
<li class="fragment">Volume</li>
<li class="fragment">Velocity</li>
<li class="fragment">Variety</li>
<li class="fragment">and Veracity and Value</li>
<li class="fragment">Too ambiguous!! Let's go beyond that</li>
</ul>
</section>
<section>
<h2>How Big is big?</h2>
<p class="fragment">Can you fit it in one computer?</p>
<p class="fragment">Yes? -> Then is not really big</p>
<p class="fragment">Let's call it big data only if it doesn't fit on one computer (and has the 3Vs)</p>
</section>
<section>
<h2>Why this criteria?</h2>
<p class="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.</p>
</section>
<section><img src="images/datafit1.svg"></section>
<section><img src="images/datafit2.svg"></section>
<section>
<h2>Example: photo collection</h2>
<ul>
<li class="fragment">One photo -> 10MB</li>
<li class="fragment">1k photos in a cellphone -> 10MB * 1k = 10000MB = 10GB</li>
<li class="fragment">50k photos in your computer -> 10MB * 50k = 500GB</li>
<li class="fragment">Is that big data?</li>
<li class="fragment">No, you can fit that in one cheap external hard drive</li>
</ul>
</section>
<section>
<h2>Problem: count how many blue photos in my collection?</h2>
<p>How do you compute this?</p>
<p class="fragment">Put all your photos in one computer</p>
<p class="fragment">Go through all the collection and count</p>
</section>
<section><img src="images/datafit_processing.svg"></section>
<section>
<h2>Flickr size</h2>
<p>80+ trillion photos (80'''000''000'000.000)</p>
<p class="fragment">That's big data</p>
</section>
<section>
<h2>How many blue photos on Flickr?</h2>
<p>How do you compute this?</p>
<p class="fragment">Distribute the data among hundreds of thousand of computers (a cluster).</p>
<p class="fragment">Compute subtotals on each chunk of the data. (Map)</p>
<p class="fragment">Aggregate the subtotals into one big total. (Reduce)</p>
</section>
<section><img src="images/dont_fit.svg"></section>
<section><img src="images/dont_fit2.svg"></section>
<section><img src="images/dont_fit_blocks.svg"></section>
<section><img src="images/dont_fit_blocks_distributed.svg"></section>
<section><img src="images/map_reduce.svg"></section>
<section>
<h2>How many computers do you need?</h2>
<p class="fragment">total / one computer capacity?</p>
<p class="fragment">What if one computer breaks down?</p>
<p class="fragment">We need redundancy -> Each photo is stored in many computers</p>
<p class="fragment">How do we control versions? How to keep records? What goes where?</p>
<p class="fragment"><strong>That's why we need big data!!</strong></p>
</section>
<section><img src="images/redundancy.svg"></section>
<section id="Technologies">
<h2>Technologies</h2>
<ul>
<li class="fragment">MapReduce (Hadoop, Hive, pig, Spark ...)</li>
<li class="fragment">NoSQL Databases (Redis, Cassandra, MongoDB, Neo4J)</li>
<li class="fragment">Distributed Relational (SQL) Databases (MySQL, PostgreSQL, Oracle, SqlServer)</li>
<li class="fragment">Many others</li>
</ul>
</section>
<section>
<h2>Hadoop</h2>
<ul>
<li>Computing platform for big data</li>
<li>Uses clusters for storing and processing the data</li>
</ul>
</section>
<section>
<h2>Hadoop Architecture</h2><img src="images/Hadoop Architecture.svg">
</section>
<section>
<h2>Spark</h2>
<p>A distributed computing alternative of to map reduce.</p>
<ul>
<li>Easier to use</li>
<li>Integrates better with traditional programming models</li>
</ul>
</section>
<section>
<h2>NoSQL Databases</h2>
<ul>
<li>Scalable storage platforms that use techniques different to traditional SQL databases</li>
<li>Sacrifices features for performance</li>
</ul>
</section>
<section>
<h2>Types of NoSQL</h2>
<ul>
<li class="fragment">Column Oriented: Cassandra, HBase, Redshift ...</li>
<li class="fragment">Key-value: Redis, memcached, Aerospike ....</li>
<li class="fragment">Document based: MongoDB, CouchDB, DynamoDB ...</li>
<li class="fragment">Graph based: Neo4J, Titan, ...</li>
</ul>
</section>
<section>
<h2>Bonus</h2>
<p><a href="https://vimeo.com/156305374">Introduction to NoSQL for Web Developers</a></p>
</section>
<section>
<h2>Distributed Relational DB</h2>
<ul>
<li>You can also use traditional databases on a distributed way.</li>
<li>Divides the database into shards.</li>
<li>Usually doesn't scale that well.</li>
</ul>
</section>
<section>
<h2>Others</h2>
<ul>
<li>Google <a href="https://cloud.google.com/dataflow/">DataFlow</a></li>
<li>Google's replacement for MapReduce based on flows.</li>
<li>Supposed to scale better.</li>
<li>AFAIK can only be used with Google's Cloud.</li>
</ul>
</section>
</section>
<section id="howToMakeSense">
<section>
<h1>Making sense</h1>
</section>
<section>
<h2>How to make sense of it?</h2>
<ul>
<li class="fragment">Statistical Analysis</li>
<li class="fragment">Machine Learning and Artificial Intelligence</li>
<li class="fragment">Visual Analytics (and data analytics)</li>
</ul>
</section>
<section>
<h3>Data Mining/Machine Learning</h3>
<p><img src="images/machine_learning_diagram.png"></p>
</section>
<section>
<h3>Information Visualization</h3>
<p><img src="images/infovis_diagram.png"></p>
</section>
<section>
<h3>Infovis + Algorithms</h3>
<p><img src="images/infovis_algorithms_diagram.png"></p>
</section>
<section>
<table id="comparisonTable">
<tr>
<td class="fragment">
<h2>Traditional</h2>
<ul>
<li>Query for known patterns</li>
<li>Display results using traditional techniques</li>
</ul><br><strong>Pros:</strong><br>
<ul>
<li>Many solutions</li>
<li>Easier to implement</li>
</ul><br><strong>Cons:</strong><br>
<ul>
<li>Can’t search for the unexpected</li>
</ul>
</td>
<td class="fragment">
<h2>Data Mining/ML</h2>
<ul>
<li>Based on statistics</li>
<li>Black box approach</li>
<li>Output outliers and correlations</li>
<li> Human out of the loop</li>
</ul><br><strong>Pros:</strong><br>
<ul>
<li> Scalable</li>
</ul><br><strong>Cons:</strong>
<ul>
<li> Analysts have to make sense of the results</li>
<li> Makes assumptions on the data</li>
</ul>
</td>
<td class="fragment">
<h2>InfoVis</h2>
<ul>
<li> Visual Interactive Interfaces</li>
<li> Human in the loop</li>
</ul><br><strong>Pros:</strong><br>
<ul>
<li> Visual bandwidth is enormous</li>
<li> Experts decided what to search for</li>
<li> Identify unknown patterns and errors in the data</li>
</ul><br><strong>Cons</strong><br>
<ul>
<li>Scalability can be an issue</li>
</ul>
</td>
</tr>
</table>
</section>
<section>
<h2>Why should we visualize?</h2>
</section>
<section>
<h2>Anscombe's quartet</h2><img src="images/anscombes.jpg">
</section>
<section>
<h2>Anscombe's quartet</h2><img src="images/anscombes2.jpg">
</section>
<section>
<h2>Anscombe's visualized</h2><img src="images/anscombes_graph.jpg">
</section>
<section>
<h2>In Infovis we look for <strong>Insights</strong></h2>
<ul>
<li class="fragment">Deep understanding</li>
<li class="fragment">Meaningful</li>
<li class="fragment">Non obvious</li>
<li class="fragment">Actionable</li>
</ul>
</section>
<section>
<h2>How do I do it?</h2><img src="images/My skills.png" class="demo">
</section>
<section id="BuscandoInsights">
<h2>What do I use?</h2><img src="images/What I use.png" class="demo">
</section>
</section>
<section>
<section>
<h1>Insights</h1>
</section>
<section id="InsightFDA">
<h2>FDA</h2>
<p>Task: Change in drug's adverse effects reports</p>
<p>User: FDA Analysts</p>
</section>
<section>
<h2>State of the art</h2><img src="images/sectorMap_drug1.png" class="demo">
</section>
<section><img src="images/side_by_side_Vasc_sectorMaps.png" class="demo"></section>
<section><img src="images/skylines_explanation.png"></section>
<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><a href="https://treeversity.cattlab.umd.edu/">https://treeversity.cattlab.umd.edu/</a></section>
<section id="InsightPARC">
<h2>Health insurance claims</h2>
<p>Task: Detect fraud networks</p>
<p>User: Undisclosed Analysts</p>
</section>
<section><img src="images/network_explorer_before.png" class="demo"></section>
<section>
<h2>Clustering</h2>
<iframe src="https://cdn.rawgit.com/john-guerra/ecdde32ab4ad91a1a240/raw/2c1a843df8631604a99140ddc9db8ee048624a79/index.html" class="blocks"></iframe>
</section>
<section>
<h2>Force in a box</h2>
<iframe src="https://cdn.rawgit.com/john-guerra/14c943d8f198d9f3fef2/raw/320474d468321d00f3241609a125c8d37935474b/index.html" class="blocks"></iframe>
</section>
<section>
<h2>Overview</h2><img src="images/overview_10k_nointer_cropped.png" class="demo">
</section>
<section>
<h2>Ego distance</h2><img src="images/network_explorer_ego_distance.png" class="demo">
</section>
<section id="InsightTweetometro">
<h2>Tweetometro</h2>
<p>Task: Twitter behavior during Presidential Elections</p>
<p>User: Me</p>
</section>
<section>
<iframe src="http://tweetometro.co" alt="images/tweetometro.png" class="blocks"></iframe><a href="http://tweetometro.co">http://tweetometro.co</a>
</section>
<section>
<h2>Normal tweets</h2><img src="http://tweetometro.co/img/all_tweets_May_25_2014.png" class="demo">
</section>
<section>
<h2>Weird tweets?</h2><img src="http://tweetometro.co/img/suspicious_tweets_May_25_2014.png" class="demo">
</section>
<section>
<h2>Creation dates</h2><img src="http://tweetometro.co/img/user_creation_date_May_25_2014.png" class="demo">
</section>
<section>
<h2>Number of followers</h2><img src="http://tweetometro.co/img/num_followers_May_25_2014.png" class="demo">
</section>
<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>
</span></section>
<section id="InsightCars">
<h2>What car to buy?</h2>
<p>Task: What's the best car to buy?</p>
<p>User: Me</p>
</section>
<section>
<h2>Normal procedure</h2>
<p>Ask friends and family</p>
</section>
<section id="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>
</span></section>
<section id="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>
</span></section>
<section id="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>
</span></section>
<section>
<h2>Problem</h2>
<p>That's inferring statistics from a sample n=1</p>
</section>
<section>
<h2>Better approach</h2>
<p>Data based decisions</p>
</section>
<section><img src="images/tucarro.png"><a href="http://tucarro.com">http://tucarro.com</a></section>
<section>
<iframe src="http://infovis.co/carrosUsados/todosPuntos.html" class="blocks"></iframe>
</section>
<section>
<iframe src="http://infovis.co/carrosUsados/depreciacionMarcas.html" class="blocks"></iframe>
</section>
<section>
<iframe src="http://infovis.co/carrosUsados/depreciacionesCarros.html" class="blocks"></iframe>
</section>
</section>
<section>
<h2>Take home message</h2>
<ul>
<li class="fragment">Big data? Sure, If it doesn't fit on a computer</li>
<li class="fragment">Finding <strong>insights</strong>, that's what matters</li>
<li class="fragment"><strong>Visual Analytics</strong>, a good way of finding insights</li>
</ul>
</section>
<section id="end">
<h1>Thank You</h1>
<h2>Questions?</h2>
<div class="contactInfo">
<p>John Alexis Guerra Gómez</p><a href="http://johnguerra.co">johnguerra.co</a><br><a href="http://twitter.com/duto_guerra">@duto_guerra</a>
</div>
</section>
<section id="Bonus">
<h1>Bonus</h1>
</section>
<section id="typesOfVisualization">
<section>
<h2>Types of Visualization</h2>
<ul>
<li>Infographics</li>
<li>Scientific Visualization (sciviz)</li>
<li>Information Visualization (infovis, datavis)</li>
</ul>
</section>
<section>
<h3>Infographics</h3><img src="images/infographics.png">
</section>
<section>
<h3>Scientific Visualization</h3>
<ul>
<li>Inherently spatial</li>
<li>2D and 3D</li>
</ul>
<p><img src="images/sciviz.png"></p>
</section>
<section>
<h3>Information Visualization</h3><img src="images/infovis_examples.png">
</section>
<section>
<h2>Infovis Basics</h2>
</section>
<section>
<h2>Visualization Mantra</h2>
<ul>
<li>Overview first</li>
<li>Zoom and Filter</li>
<li>Details on Demand</li>
</ul>
</section>
<section id="munznerstyle">
<iframe width="100%" height="500px" scrolling="no" frameborder="no" src="munzner.html"></iframe>
</section>
<section id="munznerpreference">
<h2>Perception Preference</h2>
<div id="munznerpreferencechart"></div>
<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);
});
</script>
<p class="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</a></p>
</section>
<section id="dataTypes">
<section>
<h2>Data Types</h2>
<table class="small">
<tr>
<td><strong>1-D Linear</strong></td>
<td>Document Lens, SeeSoft, Info Mural</td>
</tr>
<tr>
<td><strong> 2-D Map</strong></td>
<td>GIS, ArcView, PageMaker, Medical imagery</td>
</tr>
<tr>
<td><strong> 3-D World</strong></td>
<td>CAD, Medical, Molecules, Architecture</td>
</tr>
<tr>
<td><strong> Multi-Var</strong></td>
<td>Spotfire, Tableau, GGobi, TableLens, ParCoords,</td>
</tr>
<tr>
<td><strong> Temporal</strong></td>
<td>LifeLines, TimeSearcher, Palantir, DataMontage, LifeFlow</td>
</tr>
<tr>
<td><strong> Tree</strong></td>
<td>Cone/Cam/Hyperbolic, SpaceTree, Treemap, Treeversity</td>
</tr>
<tr>
<td><strong> Network</strong></td>
<td>Gephi, NodeXL, Sigmajs</td>
</tr>
</table>
</section>
</section>
</section>
</div>
</div>
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