{"id":926,"date":"2012-09-06T15:43:30","date_gmt":"2012-09-06T15:43:30","guid":{"rendered":"http:\/\/www.rochester.edu\/newscenter\/?p=926"},"modified":"2014-07-29T19:40:51","modified_gmt":"2014-07-29T19:40:51","slug":"whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/","title":{"rendered":"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?"},"content":{"rendered":"

Advances in computing power and a wealth of digital information are changing scientific research.<\/em><\/h4>\n

By Kathleen McGarvey<\/em><\/p>\n

In\u2004June \u2014 and\u2004in\u2004digital culture, that\u2019s already a good while ago\u2014the CEO of the social networking service Twitter, Dick Costolo, announced that users were posting 400 million tweets a day. And that was up 60 million tweets per day from the figure just three months before. It all adds up to a billion tweets every two and a half days.<\/p>\n

As a microblogging service that allows people to post messages of no more than 140 characters, Twitter is an immense but transitory compendium of observations, insights, outbursts, and mundanities. What value could it have for scientific researchers?<\/p>\n

A lot, as it happens. Henry Kautz, chair of the computer science department, and colleagues Adam Sadilek and Vincent Silenzio have shown that Twitter messages can be harnessed to predict the spread of infectious diseases, such as influenza.<\/p>\n

This year, they have published two papers explaining how, by using the geo-tags embedded in tweets, scientists can use social networking data to model the transmission of disease\u2014and even to forecast when and if a specific individual will fall ill.<\/p>\n

Kautz, Sadilek, a postdoctoral fellow in computer science, and Silenzio, associate professor of psychiatry and a member of the Department of Community and Preventive Medicine, have programmed computers to identify tweets in which people talk about feeling sick\u2014disregarding messages where people use the term figuratively.<\/p>\n

\u201cOnce you have that, you can start to map where people are sick,\u201d Kautz says, because GPS in cell phones indicate where a tweet was made. \u201cAnd you can actually start to create a visualization of the spread of disease through cities and across time.\u201d<\/p>\n

\u201cThese results provide a foundation for research on fundamental questions of public health,\u201d the team writes, \u201cincluding the identification of non-cooperative disease carriers (\u2018Typhoid Marys\u2019), adaptive vaccine policies, and our understanding of the emergence of global epidemics from day-to-day interpersonal interactions.\u201d
\nAnd they note that the approach has applicability far beyond infectious diseases, for modeling and predicting political ideas, purchasing preferences, or nearly anything else rooted in behavior.<\/p>\n

\u201cIt\u2019s actually pretty neat,\u201d Kautz says\u2014so neat that they\u2019ve formed a venture capital\u2013funded start-up business, Corpora, that makes use of the technology for applications in areas such as health care, insurance, pharmaceuticals, government agencies, and public opinion tracking.<\/p>\n

Such ingenuity, combined with vast quantities of information and high-performance computing, is changing the parameters of knowledge.<\/p>\n

We call ours the \u201cinformation age\u201d\u2014an era marked by an endless digital trail revealing what we do and where we do it, and much that\u2019s happening within us and without us.<\/p>\n

Anyone who has used the Internet is already familiar with the ways businesses have seized on that trove of information to predict and guide the choices we make as we purchase books and shoes, vacations and music.<\/p>\n

But the possibilities of \u201cbig data\u201d\u2014the fast-emerging shorthand term for the efficient analysis and problem-solving application of vast quantities of data\u2014are profound for science, medicine, and other areas of research. Through high-performance computing, creative computer science, and new bonds of collaboration, researchers find themselves at the brink of what many predict to be a new age of investigation and advances in knowledge\u2014comparable, the New York Times<\/em> has suggested, to the introduction of the microscope and the telescope.<\/p>\n

Rochester is at the forefront, pairing teams of researchers and computational scientists with supercomputing technology to transform data into knowledge.<\/p>\n

Applications range widely. Why do countries go to war? Curt Signorino, associate professor of political science, is using data mining tools drawn from genetics and finance to compare data on every combination of countries from the years 1900 to 2000, creating an explanatory model that fits the data more than three times better than standard techniques.<\/p>\n

How can energy flow through the power grid to make sure that electricity is reliably delivered to people where they need it, when they need it? Mark Bocko, professor and chair of the Department of Electrical and Computer Engineering and director of the Center for Emerging and Innovative Sciences, is studying the dynamic behavior of the power grid and how to control it with the tactical use of data\u2014what has become known as the \u201csmart grid.\u201d He and his team are developing imaging, sound, and vibration sensors that will sort through information at the source, curbing the amount of transmitted data so only the most useful is passed along.<\/p>\n

How can we better fight the flu, which claims the lives of 30,000 to 40,000 people each year in the United States? David Topham, vice provost and professor of microbiology and immunology, and colleagues are working to build a computer model of the immune system that will allow for simulations of infections and possible vaccines before the flu strikes\u2014thereby speeding production of effective vaccines, and saving lives.<\/p>\n

Pedro Domingos, associate professor of computer science at the University of Washington, who will be a featured speaker at a conference on big data to be held at Rochester in October, says there are few if any fields that will be untouched. \u201cScience, in just about every area, without big data will grind to a halt. It will be a field of diminishing returns.\u201d<\/p>\n

Kautz, who is director of an initiative for big data in Arts, Sciences & Engineering, says complex problems in science, mathematics, engineering, and the social sciences have traditionally been approached by breaking them into smaller pieces, understanding how each works, and then deducing solutions to the larger problems.<\/p>\n

But systems science\u2014a broad and interdisciplinary field underpinning big data that studies the behavior of complex physical, biological, artificial, and social systems\u2014has upturned that approach, focusing on the whole instead of the parts and ushering in a new scale for problem solving. It provides a fresh capacity to see how things interrelate and influence each other, from the molecular level to entire populations.<\/p>\n

\u201cI\u2019m an immunologist,\u201d says Topham. \u201cI was trained in cell biology, so I like to study individual cells.\u201d<\/p>\n

Formerly, he would collect a blood or tissue sample, isolate the cells, and from experiments on them, garner a few elements of data. Now, when he and his colleagues carry out clinical studies, they pursue many more dimensions of cellular investigation.<\/p>\n

Computational approaches are going to \u201callow us to identify biological relationships between cells and proteins, microorganisms and the host, that we wouldn\u2019t otherwise have been able to detect, and then understand how these affect our ability to respond to vaccines or disease,\u201d he says. A computational take on science has inverted the relationship between experimentation and analysis. Carrying out experiments used to consume about 75 percent of researchers\u2019 time, and analysis the remaining 25 percent, but \u201cI would say that\u2019s reversed now,\u201d he says. \u201cYou can do one experiment, and it will take weeks to analyze the data.\u201d<\/p>\n

While\u2004computers, computational methods, and data collection are advancing rapidly, these are still early days for big data. When researchers talk about the data now available, they seem to reach almost instinctively for metaphors of water: a deluge, a flood, a relentless torrent of information to be channeled and controlled.<\/p>\n

\u201cIt\u2019s not just more streams of data, but entirely new ones,\u201d says the Times about what it terms a \u201cdata flood.\u201d An influential report on big data issued last year by McKinsey Global Institute, the research arm of the global management consulting firm McKinsey & Company, invoked the idea of \u201clarge pools of data that can be captured, communicated, aggregated, stored, and analyzed\u201d today.<\/p>\n

\u201cWe don\u2019t know how to manage this information. It\u2019s like drinking from a fire hose\u2014how do you control it so that you don\u2019t become overwhelmed?\u201d says David Williams, dean for research for Arts, Sciences & Engineering and the William G. Allyn Professor of Medical Optics.<\/p>\n

As critical as the availability of data is the capacity to select from and organize it\u2014to sort out the most useful elements, the most meaningful patterns, the formerly unrecognized connections\u2014and transform the flood of data into something of practical value.<\/p>\n

\u201cIt\u2019s a bit like prospecting in the old days of mining, because you\u2019re looking for nuggets of gold,\u201d says Rob Clark, dean of the Hajim School and interim senior vice president for research.<\/p>\n

But it\u2019s not a passive search. \u201cI think \u2018big data\u2019 is a term, like \u2018cloud,\u2019 that\u2019s getting thrown around so much that it\u2019s getting distorted,\u201d says David Lewis, vice president for information technology and CIO. \u201cTo us, \u2018big data\u2019 is doing something with the data\u2014you\u2019re doing the analytics.\u201d<\/p>\n

Such analysis has emerged as a national priority. In March, the White House\u2019s Office of Science and Technology Policy announced a \u201cBig Data Research and Development Initiative\u201d aimed at bringing together research universities, industry, and nonprofit organizations with the federal government to take advantage of the opportunities big data offers for science and innovation.<\/p>\n

\u201cThe technology for generating new data is always far ahead of our ability to analyze it. It has become a major, global problem,\u201d says Topham. \u201cThe real data comes when you can relate different kinds of data, find the connections\u2014and that\u2019s very difficult to do. It almost requires intuition.\u201d<\/p>\n

Intuition is a tough thing to teach, but through courses in data mining, biostatistics, and algorithms, students are acquiring the skills needed to swim proficiently in a sea of data. Clark says the secret lies in teaching students the basics of how to manage information, big or small, \u201cto extract kernels of useful information from data sets.\u201d<\/p>\n

Such extraction is changing scientific research across the disciplines. In Arts, Sciences & Engineering, earth and environmental sciences and chemical engineering have to take a big data approach.<\/p>\n

\u201cWe really view it as refining the tools for supporting the mechanics of research,\u201d says Carmala Garzione, associate professor and chair of the earth and environmental sciences department. \u201cWe\u2019re basically moving from a very discipline-oriented science, where you would have a group of researchers who\u2019d look at some very specific aspect of the earth, to a much more interdisciplinary science, where groups of researchers are working across disciplinary boundaries to understand how the earth behaves as a complex system.\u201d<\/p>\n

The kind of transition she describes is one taking place across disciplines, says Washington\u2019s Domingos. \u201cI think a mental shift has to happen in how scientists think about doing science.\u201d Graduate students and researchers early in their careers have been professionally formed in an environment of computational approaches, but for more established scientists, he notes, big data requires an adjustment to a new way of pursuing research questions.<\/p>\n

At the Medical Center, it\u2019s an approach that is swiftly becoming central. The University, New York State, and IBM have partnered to establish the Health Sciences Center for Computational Innovation. It\u2019s home to the IBM Blue Gene\/Q supercomputer, making Rochester one of the five most powerful university-based supercomputing sites in the country.<\/p>\n

\u201cIt\u2019s one of the most powerful supercomputers dedicated to health research in the world,\u201d says Topham, director of the HSCCI. \u201cThe Blue Gene\/Q lets you run experiments that otherwise wouldn\u2019t be possible.\u201d<\/p>\n

For example, Jean-Philippe Couderc, associate professor of cardiology and assistant director of the Heart Research Follow-up Program Laboratory, and colleague Coeli Lopes, assistant professor at the Aab Cardiovascular Research Institute\u2014along with Jeremy Rice of IBM\u2019s Watson Research Center\u2014plan to use Blue Gene\/Q in modeling the heart to test drugs\u2019 effects on the organ.<\/p>\n

In 2004, the Food and Drug Administration launched an initiative designed to bring medical breakthroughs to patients more quickly while ensuring safety and reducing the costs of drug development. Key to that effort has been developing better ways to test the cardiac toxicity of drugs\u2014a leading cause of drugs being removed from the market.<\/p>\n

Together with the FDA, the University in 2008 established an electronic repository of electrocardiography data\u2014the Telemetric and Holter ECG Warehouse, or THEW\u2014to help foster research in the field. The database is part of the Center for Quantitative Electrocardiology and Cardiac Safety, funded by a $2.3 million grant from the National Institutes of Health and a part of the University\u2019s Heart Research Follow-up Program. It brings together an international network of academic researchers, pharmaceutical and medical device companies, and government regulators. Data from the center is provided to academic and private research organizations to help them design and validate new tools and methods to detect abnormal cardiac activity.<\/p>\n

The repository makes Rochester the hub of a heart research wheel that spans the globe, from academic institutions and industry in Europe to Asia to South America.<\/p>\n

\u201cWe are the only academic group in the world that provides an open resource of ECG data for drug safety evaluation,\u201d says Couderc. Last year alone, 25 publications were produced from repository data. Together with Lopes and Rice, Couderc is using data from THEW to model effects of drugs on cardiac cells, using a computerized model of the heart system produced by the National Library of Medicine in cooperation with IBM.<\/p>\n

From an anatomical point of view, the model\u2019s an excellent representation of the heart, he says. He and his team are being trained in using the Blue Gene\/Q computer to evaluate the effects of drugs on a wedge of the heart\u2014its inner and outer layers\u2014and the millions of different cells that form them. They check the results of the model against the documented results shown in THEW\u2019s records.<\/p>\n

\u201cIBM brings a unique tool; the University brings unique data sets, enabling such tools to have a significant impact on drug-safety evaluation and medicine,\u201d he says.<\/p>\n

But one of the most important ingredients in that equation is the imagination and inventiveness of people engaged in research.<\/p>\n

\u201cWe lead with people, not with computing,\u201d says Lewis. It\u2019s an emphasis that others echo.<\/p>\n

\u201cThis is all about the people. In fact, the people are the far more valuable and important component of this partnership,\u201d says Topham. \u201cYes, you need hardware\u2014but you can\u2019t use the hardware if you don\u2019t have the right people, and IBM has a very strong interest in disseminating the knowledge of how to use these tools to deal with important questions. \u201cOur health sciences researchers have the questions. We have the patient population. We have the ability to generate the data. We just need the tools to analyze it. And together, the University and IBM can do much more than either one of us can do on our own.\u201d<\/p>\n

The collaboration doesn\u2019t extend only to IBM researchers; big data is strengthening ties between researchers all over campus. Big data allows \u201cthe creation of teams of investigators,\u201d says Williams. \u201cI think we\u2019re going to see a lot more collaboration between investigators at different institutions\u201d because of improved communications and the ability to transmit data.<\/p>\n

The studies being carried out through HSCCI require many people, and a wide variety of expertise, says Topham. \u201cI have pediatricians, infectious disease specialists, immunologists, neonatologists, researchers in genomics and microbiomics, computational people, data management\u2014we have a huge data management core to deal with all that data.\u201d<\/p>\n

Kautz\u2019s vision for big data at Rochester focuses not on supercomputing hardware\u2014\u201cwe have that well in hand,\u201d he says\u2014but on the people who use them. \u201cWhat I think we need are more people thinking of extremely creative ways to use these machines, as well as other resources.\u201d<\/p>\n

With the rise of big data, computer scientists take on a pivotal role in the research of many fields. \u201cOne aspect of providing computational support to a physical scientist is saying, we\u2019re programmers. You tell us what to do and we\u2019ll run that package. But there\u2019s this other role in terms of helping think more deeply about the problem, because that\u2019s the new way of solving the problem. And you need to do both.\u201d<\/p>\n

Bringing big data to bear on the field of environmental sciences, for example, will \u201crequire a strong collaboration between computer scientists and earth scientists,\u201d say Garzione. \u201cUltimately, the department plans to hire \u201cearth scientists with a strong computational bent\u201d\u2014Vasilii Petrenko, an atmospheric chemist, joined the faculty last year, and John Kessler, a chemical oceanographer, came on board for this academic year\u2014\u201cor computer scientists who are capable of tackling problems in other disciplines with a healthy dialogue that enables them to find a solution to computational problems.\u201d<\/p>\n

And solutions found in one area can, at the computational level, provide keys to other areas, far afield.<\/p>\n

Algorithms that Kautz\u2019s students are developing for mining social network data from Twitter, for example, might turn out to be relevant for doing computational biology. \u201cThat kind of thing happens all the time,\u201d says Kautz. \u201cYou look at a problem with the right level of abstraction and you realize, \u2018Gee, we can think of both of these things as a network, and we\u2019re trying to find certain patterns.\u2019 \u201d<\/p>\n

In his lab, Topham says, researchers are studying vaccines and immune responses, but the methodological advances they make \u201ccould apply to cancer, to development, to cognition. They could apply it to environmental questions.\u201d<\/p>\n

The possibilities excite him.<\/p>\n

\u201cI\u2019m hoping we get to do some really important research, that we solve some long-standing questions,\u201d he says. \u201cDeveloping new vaccines. Imaging the brain better so that you can tailor treatment more effectively. Understanding individual cells\u2019 behavior, in the brain or in the immune system\u2014these are the key questions that have just been lingering out there in the field. \u201cAnd we now have the technologies to study these things in ways we didn\u2019t before.\u201d<\/p>\n

The University will host a conference, RocData: The Rochester Big Data Forum 2012, October 4\u20136. To learn more, visit www.rochester.edu\/rocdata<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

A lot, as it happens. Henry Kautz, chair of the computer science department, and his colleagues have shown that Twitter messages can be harnessed to predict the spread of infectious diseases, for example.<\/p>\n","protected":false},"author":4,"featured_media":1706,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[18612,11716,15512,19232],"class_list":["post-926","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sci-tech","tag-announcements","tag-data-science","tag-henry-kautz","tag-social-media"],"acf":[],"yoast_head":"\nWhat\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?<\/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:\/\/www.rochester.edu\/newscenter\/whats-big-data-got\u2004to\u2004do\u2004with\u2004it\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?\" \/>\n<meta property=\"og:description\" content=\"A lot, as it happens. Henry Kautz, chair of the computer science department, and his colleagues have shown that Twitter messages can be harnessed to predict the spread of infectious diseases, for example.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got\u2004to\u2004do\u2004with\u2004it\/\" \/>\n<meta property=\"og:site_name\" content=\"News Center\" \/>\n<meta property=\"article:published_time\" content=\"2012-09-06T15:43:30+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2014-07-29T19:40:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2012\/09\/fea_bigdata11.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"600\" \/>\n\t<meta property=\"og:image:height\" content=\"300\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Scott Hauser\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Scott Hauser\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"15 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/\"},\"author\":{\"name\":\"Scott Hauser\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/#\\\/schema\\\/person\\\/450009e21cedd5bdc4165eb28a6e8df4\"},\"headline\":\"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?\",\"datePublished\":\"2012-09-06T15:43:30+00:00\",\"dateModified\":\"2014-07-29T19:40:51+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/\"},\"wordCount\":2986,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/wp-content\\\/uploads\\\/2012\\\/09\\\/fea_bigdata11.jpg\",\"keywords\":[\"announcements\",\"data science\",\"Henry Kautz\",\"social media\"],\"articleSection\":[\"Science & Technology\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/\",\"url\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/\",\"name\":\"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/wp-content\\\/uploads\\\/2012\\\/09\\\/fea_bigdata11.jpg\",\"datePublished\":\"2012-09-06T15:43:30+00:00\",\"dateModified\":\"2014-07-29T19:40:51+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/#\\\/schema\\\/person\\\/450009e21cedd5bdc4165eb28a6e8df4\"},\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/wp-content\\\/uploads\\\/2012\\\/09\\\/fea_bigdata11.jpg\",\"contentUrl\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/wp-content\\\/uploads\\\/2012\\\/09\\\/fea_bigdata11.jpg\",\"width\":600,\"height\":300},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/#website\",\"url\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/\",\"name\":\"News Center\",\"description\":\"University of Rochester\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/#\\\/schema\\\/person\\\/450009e21cedd5bdc4165eb28a6e8df4\",\"name\":\"Scott Hauser\",\"url\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/author\\\/shauser\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?","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:\/\/www.rochester.edu\/newscenter\/whats-big-data-got\u2004to\u2004do\u2004with\u2004it\/","og_locale":"en_US","og_type":"article","og_title":"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?","og_description":"A lot, as it happens. Henry Kautz, chair of the computer science department, and his colleagues have shown that Twitter messages can be harnessed to predict the spread of infectious diseases, for example.","og_url":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got\u2004to\u2004do\u2004with\u2004it\/","og_site_name":"News Center","article_published_time":"2012-09-06T15:43:30+00:00","article_modified_time":"2014-07-29T19:40:51+00:00","og_image":[{"url":"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2012\/09\/fea_bigdata11.jpg","width":600,"height":300,"type":"image\/jpeg"}],"author":"Scott Hauser","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Scott Hauser","Est. reading time":"15 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#article","isPartOf":{"@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/"},"author":{"name":"Scott Hauser","@id":"https:\/\/www.rochester.edu\/newscenter\/#\/schema\/person\/450009e21cedd5bdc4165eb28a6e8df4"},"headline":"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?","datePublished":"2012-09-06T15:43:30+00:00","dateModified":"2014-07-29T19:40:51+00:00","mainEntityOfPage":{"@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/"},"wordCount":2986,"commentCount":0,"image":{"@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#primaryimage"},"thumbnailUrl":"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2012\/09\/fea_bigdata11.jpg","keywords":["announcements","data science","Henry Kautz","social media"],"articleSection":["Science & Technology"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/","url":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/","name":"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?","isPartOf":{"@id":"https:\/\/www.rochester.edu\/newscenter\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#primaryimage"},"image":{"@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#primaryimage"},"thumbnailUrl":"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2012\/09\/fea_bigdata11.jpg","datePublished":"2012-09-06T15:43:30+00:00","dateModified":"2014-07-29T19:40:51+00:00","author":{"@id":"https:\/\/www.rochester.edu\/newscenter\/#\/schema\/person\/450009e21cedd5bdc4165eb28a6e8df4"},"breadcrumb":{"@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#primaryimage","url":"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2012\/09\/fea_bigdata11.jpg","contentUrl":"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2012\/09\/fea_bigdata11.jpg","width":600,"height":300},{"@type":"BreadcrumbList","@id":"https:\/\/www.rochester.edu\/newscenter\/whats-big-data-got%e2%80%84to%e2%80%84do%e2%80%84with%e2%80%84it\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.rochester.edu\/newscenter\/"},{"@type":"ListItem","position":2,"name":"What\u2019s Big Data Got\u2004to\u2004Do\u2004with\u2004It?"}]},{"@type":"WebSite","@id":"https:\/\/www.rochester.edu\/newscenter\/#website","url":"https:\/\/www.rochester.edu\/newscenter\/","name":"News Center","description":"University of Rochester","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.rochester.edu\/newscenter\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.rochester.edu\/newscenter\/#\/schema\/person\/450009e21cedd5bdc4165eb28a6e8df4","name":"Scott Hauser","url":"https:\/\/www.rochester.edu\/newscenter\/author\/shauser\/"}]}},"_links":{"self":[{"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/posts\/926","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/comments?post=926"}],"version-history":[{"count":0,"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/posts\/926\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/media\/1706"}],"wp:attachment":[{"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/media?parent=926"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/categories?post=926"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.rochester.edu\/newscenter\/wp-json\/wp\/v2\/tags?post=926"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}