{"id":7843,"date":"2014-09-23T06:51:18","date_gmt":"2014-09-23T04:51:18","guid":{"rendered":"https:\/\/uniavisen.dk\/?p=7843\/"},"modified":"2017-01-20T22:50:35","modified_gmt":"2017-01-20T22:50:35","slug":"big-data-big-challenges-big-hopes","status":"publish","type":"post","link":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/","title":{"rendered":"Big data, big challenges, big hopes"},"content":{"rendered":"

Advances in big data search, collection, and processing, promise services to mankind that so far have not been imagined. The data can provide insight into medical research and health care, climate phenomena, and social inequalities. And it can reveal business ideas that nobody ever thought of before.<\/p>\n

This is according to Corinna Cortes, head of Google Research, NY, and associate professor at the University of Copenhagen, who has just been awarded an honorary alumni prize. She will be in Copenhagen to receive the prize on 27 September at the Alumni Day event.<\/a> <\/p>\n

\u201cTechnically, Big Data is simply a lot of data,\u201d Corinna Cortes explains to the University Post.<\/p>\n

\u201cLet me give an example. Every morning you may check the outdoor temperature, the barometric pressure, and look at how much it rained yesterday. That is small data. Add to this the continuous electronic records of these numbers from millions of locations around the world. Add again to this satellite imagery of all of the Earth and water flow measurements of our oceans and you get Big Data.\u201d<\/p>\n

Hurricane Sandy and street view<\/h2>\n

Big Data (see box right) is defined by a bunch of V\u2019s: It is high \u2018Volume\u2019 – there is lots of it. It has \u2018Variety\u2019 – it mixes text, dates, numbers, images, videos. It may not have \u2018Veracity\u2019 – meaning it is messy, raw, low-level, detailed and noisy data.<\/p>\n

And then it has \u2018Velocity\u2019, or as Corinna puts it: \u201cIt keeps coming at us like open fire hoses\u201d.<\/p>\n

Corinna Cortes: \u201cBig Data is so popular that a whole new education as a Data Scientist has emerged\u201d<\/p><\/blockquote>\n

Here is an example. Combining temperature observations, ozone measurements, moisture maps, and satellite imagery pose immense computational challenges, but the payoff may be significant. If we can learn how to process all this data and form models we may faster detect changes in our climate and be able take corrective measures. But even better short term weather predictions, such as tsunami warnings are important. <\/p>\n

Corinna Cortes offers a less dramatic example from her own life. <\/p>\n

\u201cLess than 2 years ago New York City was hit by hurricane Sandy. The predictions focused on the wind strengths, but it was actually the rise in water level that caused the biggest problems. We were ourselves without water, heat, and electricity for 6 days, and many were without it for much longer.”<\/p>\n

\u201cSince then we have seen many visualization tools overlaying rising water levels on Street View images so one can \u2018first hand\u2019 inspect the consequences of flooding,\u201d she says. <\/p>\n

Endangered species<\/h2>\n

\u201cWe see environmental research beginning to show up as collaborations between universities and industries. Google is itself contributing with its Crisis Response Efforts<\/a> providing early warnings and public alerts.\u201d<\/p>\n

\u201cAnother great example of how Big Data can help us understand environmental changes are projects monitoring threatened animals. Various non-profit organizations use near real-time big data analysis of camera data and climate sensors to generate trends data on endangered species.\u201d<\/p>\n

Great possibilities, but also great challenges from a computational perspective.<\/p>\n

\u201cOne definition of Big Data is exactly that it is too big to fit on any conventional data analysis platform\u201d, says Corinna Cortes, adding that \u201cBig Data is so popular that a whole new education as a Data Scientist has emerged. Around 200 universities around the world are offering this new degree with a curriculum including elements from computer science, statistics, and business understanding.\u201d<\/p>\n

Search, but also Analytics, Gmail, Google Earth<\/h2>\n

One thing is climate and nature, another popular area for Big Data is what is referred to as Urban Data. Urban Data covers everything from transit analysis, noise level recordings, hospital utilization, power distribution, and trash collection. <\/p>\n

\u201cThe goal is to make our cities more efficient and effective, to increase quality of life and prevent problems\u201d, she says. \u201cI recently heard of a study that – not surprisingly – found that building fires were more frequent in buildings that hadn\u2019t had a fire inspections for some time.\u201d<\/p>\n

\u201cThe hope is that this new degree of Data Scientists will teach the students to translate ill-posed concerns into data-oriented questions and extract the information from the Big Data,\u201d says Corinna Cortes.<\/p>\n

Google is almost synonymous with \u2018search\u2019, and access to information has so far been the biggest way that Google has changed our lives, by any reckoning. But Google\u2019s other programmes range from Google Analytics for editors and website managers – tracking website traffic in real time, the ubiquitous Gmail, and Google Earth – that has turned us all into armchair travellers. To name just a few.<\/p>\n

Fighting the spam<\/h2>\n

Corinna Cortes has made her academic career in the field of machine learning. In Google this finds practical application in almost all parts of Google.<\/p>\n

\u201cWe have classifiers for speech recognition, image labeling, spam, and much, much more. Google is a paradise for machine learning,\u201d she says.<\/p>\n

\u201cA recent learning problem from my team has been that of finding the good tables on the web. Most tables are formatting tables, and we were looking to find tables that are actually about something and have good information in them. It is reasonably easy for humans to tell good tables from bad tables, so we had a lot of tables labeled and trained classifiers to tell them apart. In the process we developed new learning algorithms,\u201d she explains. A new Google structured snippets feature<\/a> uses these principles.<\/p>\n

Corinna Cortes is currently training for the New York City Marathon.\u201cThis may be my only chance ever to beat Caroline Wozniacki, the Danish tennis player that this year will also run\u201d.<\/p><\/blockquote>\n

The last time the University Post interviewed Corinna Cortes<\/a> she was searching for the best method to teach computers how to find similarities between things like words and images. Since then her team has moved forward.<\/p>\n

\u201cWe have developed algorithms for clustering of items in a changing setting. One of the first applications has been gmail spam. New sort of spam pops up all the time and we quickly have to find clusters of similar spam messages so we can start filtering them out. It is easy enough if you look at the end of the day to locate new clusters, but we have to detect them much, much faster. To do that we have developed new similarity schemes and the new algorithms reduce user complaints with hundreds of thousands a day.<\/p>\n

To run New York City Marathon<\/h2>\n

For almost every activity there is an app – a Google one, even.<\/p>\n

Outside the office, Corinna Cortes is into running. She is currently training for the New York City Marathon.<\/p>\n

\u201cThis may be my only chance ever to beat Caroline Wozniacki, the Danish tennis player that this year will also run this marathon\u201d.<\/p>\n

For her training, \u201cI just bring my phone and keys with me,\u201d she told the University Post, the last time we interviewed her.<\/p>\n

\u201cBut I love Gmap-pedometer built on Google maps<\/a>. I use it to map out my runs,\u201d she says.<\/p>\n

Read another interview with Corinna Cortes on the subject of Big Data and machine learning here.<\/a><\/p>\n

miy@adm.ku.dk<\/p>\n

Like us on Facebook <\/a> for features, guides and tips on upcoming events. Follow us on Twitter<\/a> for links to other Copenhagen academia news stories. Sign up for the University Post weekly newsletter here<\/a>, and then follow the University Post on Instagram here. <\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Machine learning promises to help us understand climate changes, monitor threatened animals, improve quality of life, and in general increase our knowledge of society. Head of Google Research, NY, and University of Copenhagen honorary alumni believes there are huge opportunities in Big Data<\/p>\n","protected":false},"author":12,"featured_media":7845,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[46],"tags":[],"class_list":["post-7843","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","expression-news_article"],"acf":[],"aioseo_notices":[],"yoast_head":"\nBig data, big challenges, big hopes<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Big data, big challenges, big hopes\" \/>\n<meta property=\"og:description\" content=\"Machine learning promises to help us understand climate changes, monitor threatened animals, improve quality of life, and in general increase our knowledge of society. Head of Google Research, NY, and University of Copenhagen honorary alumni believes there are huge opportunities in Big Data\" \/>\n<meta property=\"og:url\" content=\"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/\" \/>\n<meta property=\"og:site_name\" content=\"University Post\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uniavis\" \/>\n<meta property=\"article:published_time\" content=\"-0001-11-30T00:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2017-01-20T22:50:35+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"MIGRATED_ARTICLES FROM_OLD_SITE\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Uniavisen\" \/>\n<meta name=\"twitter:site\" content=\"@Uniavisen\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"MIGRATED_ARTICLES FROM_OLD_SITE\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/\"},\"author\":{\"name\":\"MIGRATED_ARTICLES FROM_OLD_SITE\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#\\\/schema\\\/person\\\/b4df0b22f9be3943039e58e94c400606\"},\"headline\":\"Big data, big challenges, big hopes\",\"datePublished\":\"-0001-11-30T00:00:00+00:00\",\"dateModified\":\"2017-01-20T22:50:35+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/\"},\"wordCount\":1230,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2014\\\/09\\\/bigdata_0.jpg\",\"articleSection\":[\"Science\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/\",\"url\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/\",\"name\":\"Big data, big challenges, big hopes\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2014\\\/09\\\/bigdata_0.jpg\",\"datePublished\":\"-0001-11-30T00:00:00+00:00\",\"dateModified\":\"2017-01-20T22:50:35+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#\\\/schema\\\/person\\\/b4df0b22f9be3943039e58e94c400606\"},\"breadcrumb\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#primaryimage\",\"url\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2014\\\/09\\\/bigdata_0.jpg\",\"contentUrl\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2014\\\/09\\\/bigdata_0.jpg\",\"width\":2560,\"height\":1707,\"caption\":\"According to one definition of Big Data - it is the data set that is too big to fit any conventional data analysis platform\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/big-data-big-challenges-big-hopes\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Big data, big challenges, big hopes\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#website\",\"url\":\"https:\\\/\\\/uniavisen.dk\\\/\",\"name\":\"University Post\",\"description\":\"Independent of management\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/uniavisen.dk\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#\\\/schema\\\/person\\\/b4df0b22f9be3943039e58e94c400606\",\"name\":\"MIGRATED_ARTICLES FROM_OLD_SITE\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/b6c147fc36e92c08c95515aba962dbc89107ed33613c690182f7e243d0c0a2ab?s=96&d=identicon&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/b6c147fc36e92c08c95515aba962dbc89107ed33613c690182f7e243d0c0a2ab?s=96&d=identicon&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/b6c147fc36e92c08c95515aba962dbc89107ed33613c690182f7e243d0c0a2ab?s=96&d=identicon&r=g\",\"caption\":\"MIGRATED_ARTICLES FROM_OLD_SITE\"},\"url\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/author\\\/migrated_articles\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Big data, big challenges, big hopes","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/","og_locale":"en_US","og_type":"article","og_title":"Big data, big challenges, big hopes","og_description":"Machine learning promises to help us understand climate changes, monitor threatened animals, improve quality of life, and in general increase our knowledge of society. Head of Google Research, NY, and University of Copenhagen honorary alumni believes there are huge opportunities in Big Data","og_url":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/","og_site_name":"University Post","article_publisher":"https:\/\/www.facebook.com\/uniavis","article_published_time":"-0001-11-30T00:00:00+00:00","article_modified_time":"2017-01-20T22:50:35+00:00","og_image":[{"width":2560,"height":1707,"url":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","type":"image\/jpeg"}],"author":"MIGRATED_ARTICLES FROM_OLD_SITE","twitter_card":"summary_large_image","twitter_creator":"@Uniavisen","twitter_site":"@Uniavisen","twitter_misc":{"Written by":"MIGRATED_ARTICLES FROM_OLD_SITE","Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#article","isPartOf":{"@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/"},"author":{"name":"MIGRATED_ARTICLES FROM_OLD_SITE","@id":"https:\/\/uniavisen.dk\/#\/schema\/person\/b4df0b22f9be3943039e58e94c400606"},"headline":"Big data, big challenges, big hopes","datePublished":"-0001-11-30T00:00:00+00:00","dateModified":"2017-01-20T22:50:35+00:00","mainEntityOfPage":{"@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/"},"wordCount":1230,"commentCount":0,"image":{"@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#primaryimage"},"thumbnailUrl":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","articleSection":["Science"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/","url":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/","name":"Big data, big challenges, big hopes","isPartOf":{"@id":"https:\/\/uniavisen.dk\/#website"},"primaryImageOfPage":{"@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#primaryimage"},"image":{"@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#primaryimage"},"thumbnailUrl":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","datePublished":"-0001-11-30T00:00:00+00:00","dateModified":"2017-01-20T22:50:35+00:00","author":{"@id":"https:\/\/uniavisen.dk\/#\/schema\/person\/b4df0b22f9be3943039e58e94c400606"},"breadcrumb":{"@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#primaryimage","url":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","contentUrl":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","width":2560,"height":1707,"caption":"According to one definition of Big Data - it is the data set that is too big to fit any conventional data analysis platform"},{"@type":"BreadcrumbList","@id":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/uniavisen.dk\/en\/"},{"@type":"ListItem","position":2,"name":"Big data, big challenges, big hopes"}]},{"@type":"WebSite","@id":"https:\/\/uniavisen.dk\/#website","url":"https:\/\/uniavisen.dk\/","name":"University Post","description":"Independent of management","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/uniavisen.dk\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/uniavisen.dk\/#\/schema\/person\/b4df0b22f9be3943039e58e94c400606","name":"MIGRATED_ARTICLES FROM_OLD_SITE","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/b6c147fc36e92c08c95515aba962dbc89107ed33613c690182f7e243d0c0a2ab?s=96&d=identicon&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/b6c147fc36e92c08c95515aba962dbc89107ed33613c690182f7e243d0c0a2ab?s=96&d=identicon&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/b6c147fc36e92c08c95515aba962dbc89107ed33613c690182f7e243d0c0a2ab?s=96&d=identicon&r=g","caption":"MIGRATED_ARTICLES FROM_OLD_SITE"},"url":"https:\/\/uniavisen.dk\/en\/author\/migrated_articles\/"}]}},"advancedCustomFields":{"layout_group":[{"acf_fc_layout":"Headline","use_post_title":true,"headline":"","style":"default","highlighted_words":"","text_size":"medium"},{"acf_fc_layout":"Image","image":{"ID":7845,"id":7845,"title":"bigdata_0","filename":"bigdata_0.jpg","filesize":617677,"url":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","link":"https:\/\/uniavisen.dk\/en\/big-data-big-challenges-big-hopes\/bigdata_0\/","alt":"","author":"0","description":"According to one definition of Big Data - it is the data set that is too big to fit any conventional data analysis platform","caption":"According to one definition of Big Data - it is the data set that is too big to fit any conventional data analysis platform","name":"bigdata_0","status":"inherit","uploaded_to":7843,"date":"2017-01-19 06:28:54","modified":"2017-01-19 06:28:58","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/uniavisen.dk\/wp-includes\/images\/media\/default.png","width":2560,"height":1707,"sizes":{"thumbnail":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-150x150.jpg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-480x320.jpg","medium-width":480,"medium-height":320,"medium_large":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-768x512.jpg","medium_large-width":768,"medium_large-height":512,"large":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-1280x853.jpg","large-width":1280,"large-height":853,"1536x1536":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","1536x1536-width":1536,"1536x1536-height":1024,"2048x2048":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0.jpg","2048x2048-width":2048,"2048x2048-height":1366,"featured-soft":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-290x193.jpg","featured-soft-width":290,"featured-soft-height":193,"featured-hard":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-290x180.jpg","featured-hard-width":290,"featured-hard-height":180,"narrow":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-700x467.jpg","narrow-width":700,"narrow-height":467,"extended":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-990x660.jpg","extended-width":990,"extended-height":660}},"style":"screen","text_placement":"metadata-below","image_link_url":"","image_link_title":"","caption_prefix":"","enable_alternative_caption":false,"alternative_caption":""},{"acf_fc_layout":"Standfirst","subject":"","text":"Machine learning promises to help us understand climate changes, monitor threatened animals, improve quality of life, and in general increase our knowledge of society. Head of Google Research, NY, and University of Copenhagen honorary alumni believes there are huge opportunities in Big Data","use_post_excerpt":false},{"acf_fc_layout":"Byline","is_author":false,"contributors":[{"use_registered_user":false,"user":false,"contributor_name":"Mike Young","contributor_title":" ","contributor_image":false}]},{"acf_fc_layout":"Content","content":"<p>Advances in big data search, collection, and processing, promise services to mankind that so far have not been imagined. The data can provide insight into medical research and health care, climate phenomena, and social inequalities. And it can reveal business ideas that nobody ever thought of before.<\/p>\n<p>This is according to Corinna Cortes, head of Google Research, NY, and associate professor at the University of Copenhagen, who has just been awarded an honorary alumni prize. She will be in Copenhagen to receive the prize on 27 September at <a href=\"http:\/\/alumni.ku.dk\/alumnernesdag\/\" target=\"_blank\">the Alumni Day event.<\/a> <\/p>\n<p>\u201cTechnically, Big Data is simply a lot of data,\u201d Corinna Cortes explains to the University Post.<\/p>\n<p>\u201cLet me give an example. Every morning you may check the outdoor temperature, the barometric pressure, and look at how much it rained yesterday. That is small data. Add to this the continuous electronic records of these numbers from millions of locations around the world. Add again to this satellite imagery of all of the Earth and water flow measurements of our oceans and you get Big Data.\u201d<\/p>\n<h2>Hurricane Sandy and street view<\/h2>\n<p>Big Data (see box right) is defined by a bunch of V\u2019s: It is high \u2018Volume\u2019 – there is lots of it. It has \u2018Variety\u2019 – it mixes text, dates, numbers, images, videos. It may not have \u2018Veracity\u2019 – meaning it is messy, raw, low-level, detailed and noisy data.<\/p>\n<p>And then it has \u2018Velocity\u2019, or as Corinna puts it: \u201cIt keeps coming at us like open fire hoses\u201d.<\/p>\n<blockquote><p>Corinna Cortes: \u201cBig Data is so popular that a whole new education as a Data Scientist has emerged\u201d<\/p><\/blockquote>\n<p>Here is an example. Combining temperature observations, ozone measurements, moisture maps, and satellite imagery pose immense computational challenges, but the payoff may be significant. If we can learn how to process all this data and form models we may faster detect changes in our climate and be able take corrective measures. But even better short term weather predictions, such as tsunami warnings are important. <\/p>\n<p>Corinna Cortes offers a less dramatic example from her own life. <\/p>\n<p>\u201cLess than 2 years ago New York City was hit by hurricane Sandy. The predictions focused on the wind strengths, but it was actually the rise in water level that caused the biggest problems. We were ourselves without water, heat, and electricity for 6 days, and many were without it for much longer.”<\/p>\n<p>\u201cSince then we have seen many visualization tools overlaying rising water levels on Street View images so one can \u2018first hand\u2019 inspect the consequences of flooding,\u201d she says. <\/p>\n<h2>Endangered species<\/h2>\n<p>\u201cWe see environmental research beginning to show up as collaborations between universities and industries. Google is itself contributing with its <a href=\"https:\/\/www.google.org\/crisisresponse\/\" target=\"_blank\">Crisis Response Efforts<\/a> providing early warnings and public alerts.\u201d<\/p>\n<p>\u201cAnother great example of how Big Data can help us understand environmental changes are projects monitoring threatened animals. Various non-profit organizations use near real-time big data analysis of camera data and climate sensors to generate trends data on endangered species.\u201d<\/p>\n<p>Great possibilities, but also great challenges from a computational perspective.<\/p>\n<p>\u201cOne definition of Big Data is exactly that it is too big to fit on any conventional data analysis platform\u201d, says Corinna Cortes, adding that \u201cBig Data is so popular that a whole new education as a Data Scientist has emerged. Around 200 universities around the world are offering this new degree with a curriculum including elements from computer science, statistics, and business understanding.\u201d<\/p>\n<h2>Search, but also Analytics, Gmail, Google Earth<\/h2>\n<p>One thing is climate and nature, another popular area for Big Data is what is referred to as Urban Data. Urban Data covers everything from transit analysis, noise level recordings, hospital utilization, power distribution, and trash collection. <\/p>\n<p>\u201cThe goal is to make our cities more efficient and effective, to increase quality of life and prevent problems\u201d, she says. \u201cI recently heard of a study that – not surprisingly – found that building fires were more frequent in buildings that hadn\u2019t had a fire inspections for some time.\u201d<\/p>\n<p>\u201cThe hope is that this new degree of Data Scientists will teach the students to translate ill-posed concerns into data-oriented questions and extract the information from the Big Data,\u201d says Corinna Cortes.<\/p>\n<p>Google is almost synonymous with \u2018search\u2019, and access to information has so far been the biggest way that Google has changed our lives, by any reckoning. But Google\u2019s other programmes range from Google Analytics for editors and website managers – tracking website traffic in real time, the ubiquitous Gmail, and Google Earth – that has turned us all into armchair travellers. To name just a few.<\/p>\n<h2>Fighting the spam<\/h2>\n<p>Corinna Cortes has made her academic career in the field of machine learning. In Google this finds practical application in almost all parts of Google.<\/p>\n<p>\u201cWe have classifiers for speech recognition, image labeling, spam, and much, much more. Google is a paradise for machine learning,\u201d she says.<\/p>\n<p>\u201cA recent learning problem from my team has been that of finding the good tables on the web. Most tables are formatting tables, and we were looking to find tables that are actually about something and have good information in them. It is reasonably easy for humans to tell good tables from bad tables, so we had a lot of tables labeled and trained classifiers to tell them apart. In the process we developed new learning algorithms,\u201d she explains. A new Google <a href=\"http:\/\/googleresearch.blogspot.dk\/2014\/09\/introducing-structured-snippets-now.html\" target=\"_blank\">structured snippets feature<\/a> uses these principles.<\/p>\n<blockquote><p>Corinna Cortes is currently training for the New York City Marathon.\u201cThis may be my only chance ever to beat Caroline Wozniacki, the Danish tennis player that this year will also run\u201d.<\/p><\/blockquote>\n<p>The<a href=\"http:\/\/universitypost.dk\/article\/copenhagens-running-google-professor\" target=\"_blank\"> last time the University Post interviewed Corinna Cortes<\/a> she was searching for the best method to teach computers how to find similarities between things like words and images. Since then her team has moved forward.<\/p>\n<p>\u201cWe have developed algorithms for clustering of items in a changing setting. One of the first applications has been gmail spam. New sort of spam pops up all the time and we quickly have to find clusters of similar spam messages so we can start filtering them out. It is easy enough if you look at the end of the day to locate new clusters, but we have to detect them much, much faster. To do that we have developed new similarity schemes and the new algorithms reduce user complaints with hundreds of thousands a day.<\/p>\n<h2>To run New York City Marathon<\/h2>\n<p>For almost every activity there is an app – a Google one, even.<\/p>\n<p>Outside the office, Corinna Cortes is into running. She is currently training for the New York City Marathon.<\/p>\n<p>\u201cThis may be my only chance ever to beat Caroline Wozniacki, the Danish tennis player that this year will also run this marathon\u201d.<\/p>\n<p> For her training, \u201cI just bring my phone and keys with me,\u201d she told the University Post, the last time we interviewed her.<\/p>\n<p>\u201cBut I love <a href=\"http:\/\/gmap-pedometer.com\/\" target=\"_blank\">Gmap-pedometer built on Google maps<\/a>. I use it to map out my runs,\u201d she says.<\/p>\n<p>Read another interview with <a href=\"http:\/\/universitypost.dk\/article\/copenhagens-running-google-professor\" target=\"_blank\">Corinna Cortes on the subject of Big Data and machine learning here.<\/a><\/p>\n<p>miy@adm.ku.dk<\/p>\n<p><em>Like us on <a href=\"https:\/\/www.facebook.com\/UniversityPost\" target=\"_blank\"> Facebook <\/a> for features, guides and tips on upcoming events. Follow us on <a href=\"https:\/\/twitter.com\/UniversityPost\" target=\"_blank\"> Twitter<\/a> for links to other Copenhagen academia news stories. <a href=\"http:\/\/universitypost.dk\/newsletter\" target=\"_blank\">Sign up for the University Post weekly newsletter here<\/a>, and then <a href=\"http:\/\/instagram.com\/universitypost\" target=\"_blank\">follow the University Post on Instagram here. <\/a><\/em><\/p>\n"},{"acf_fc_layout":"ArticleEnd"},{"acf_fc_layout":"OtherStories","headline":"","hand_picked_posts":false,"references":false,"category":false,"theme":false,"number_of_posts":"4","style":"default"}]},"taxonomyData":{"category":[{"term_id":46,"name":"Science","slug":"science","term_group":0,"term_taxonomy_id":46,"taxonomy":"category","description":"","parent":0,"count":831,"filter":"raw"}],"post_tag":[],"post_format":[],"expression":[{"term_id":15,"name":"News Article","slug":"news_article","term_group":0,"term_taxonomy_id":15,"taxonomy":"expression","description":"","parent":0,"count":11492,"filter":"raw"}],"translation_priority":[]},"featured_media_url":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2014\/09\/bigdata_0-1280x853.jpg","_links":{"self":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts\/7843","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/comments?post=7843"}],"version-history":[{"count":2,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts\/7843\/revisions"}],"predecessor-version":[{"id":33104,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts\/7843\/revisions\/33104"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/media\/7845"}],"wp:attachment":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/media?parent=7843"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/categories?post=7843"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/tags?post=7843"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}