{"342491":{"#nid":"342491","#data":{"type":"news","title":"Dynamic graph analytics tackle social media and other big data","body":[{"value":"\u003Cp\u003EToday, petabytes of digital information are generated daily by such sources as social media, Internet activity, surveillance sensors, and advanced research instruments. The results are often referred to as \u201cbig data\u201d \u2013 accumulations so huge that highly sophisticated computer techniques are required to identify useful information hidden within.\u003C\/p\u003E\u003Cp\u003EGraph analysis is a prime tool for finding the needle in the data haystack. This potent technology \u2013 not to be confused with simple illustrations like bar graphs and pie charts \u2013 utilizes mathematical techniques that represent relationships in the data more efficiently than traditional statistical analyses.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EResearchers at the Georgia Tech Research Institute (GTRI) are bringing graph analytics to bear on a range of data-related challenges. They\u0027re developing advanced technology that can help investigate social networks, surveillance intelligence, computer-network functionality, industrial control systems, and more.\u003C\/p\u003E\u003Cp\u003E\u0022Our first task is to look at the interesting properties of a graph \u2013 to find the important questions we can ask of that graph,\u0022 said Dan Campbell, a GTRI principal research engineer who heads the High Performance Computing Branch. \u0022The second task is to find the answers as quickly as possible, and then put them to practical use.\u0022\u003C\/p\u003E\u003Cp\u003EA graph is a type of data structure comprised of entities \u2013 meaning anything that can be represented digitally \u2013 and their relationships. In graph terminology, an entity is a vertex or a node; the connections between it and other vertices are edges or arcs. Graphs are constructed using software algorithms that represent both the data points and the relationships between them, and also enable computers to manipulate and analyze that information.\u003C\/p\u003E\u003Cp\u003EGTRI researchers make extensive use of a graph-analysis framework called STINGER, built specifically to tackle dynamic, ever-changing applications such as social networks and Internet traffic. STINGER was created by a team led by David A. Bader, a professor in the School of Computational Science and Engineering; key members of that team included David Ediger and Robert McColl, who are now part of Campbell\u0027s GTRI group. STINGER, which is open-source software (STINGERgraph.com), continues to be developed at Georgia Tech and in the broader graph analytics community.\u003C\/p\u003E\u003Cp\u003E\u0022We\u0027ve done a great deal of work on analyzing openly available social media in real time,\u0022 said Ediger.\u0022Social media analysis clearly has an important role to play in emergency response to both natural disasters like Hurricane Sandy and to potential terrorist attacks, and we\u0027re actively researching applications in those areas, among others.\u0022\u003C\/p\u003E\u003Cp\u003ESTINGER helps support GTRI\u2019s focus on streaming or dynamic-graph technology, which can store very large databases and then update them in real time as new data come in. This novel approach allows users to monitor social media on a massive scale, and can also be utilized to simulate very large networks.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Tech researchers have presented this technology at several recent conferences including the 1st Workshop on Parallel Programming for Analytics Applications, which was held in February in Orlando, Fla., in conjunction with the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming.\u003C\/p\u003E\u003Cp\u003E\u0022Unlike traditional graph databases, STINGER\u2019s streaming-graph technology lets us store very big graphs and analyze them at high speed using fairly modest computing capability,\u0022 said Jason Poovey, a GTRI research scientist in Campbell\u0027s group. \u0022In half a terabyte of main memory \u2013 a pretty reasonable size today \u2013 we can handle billions of nodes and edges. Our benchmark tests show we can represent, update and analyze a graph in real time that\u0027s essentially the size of all the data in Twitter.\u0022\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGTRI is focusing on multiple efforts in which graph analysis plays a key role.\u0026nbsp; These projects include:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EBehavioral Modeling and Computational Social Systems (BMCSS) Strategic Initiative \u2013 A GTRI team led by senior research scientist Erica Briscoe has used STINGER to study real-time social media analytics, as part of research aimed at predicting human behavior on a large scale. \u0026nbsp;\u003C\/li\u003E\u003Cli\u003EBlackForest \u2013 Members of Campbell\u0027s group are using graph analytics to support the BlackForest project led by GTRI researcher Chris Smoak. The aim of this externally funded project involves forming coherent intelligence pictures from disparate types of data obtained from multiple sources. \u0026nbsp;\u003C\/li\u003E\u003Cli\u003ENextcache \u2013 This externally funded project focuses on developing new CPU, cache and memory designs tailored for graph-based applications.\u003C\/li\u003E\u003Cli\u003EReal-time Business Intelligence \u2013 Using streaming graph technology, members of Campbell\u2019s group are working with GTRI researcher Erica Briscoe to develop a business-intelligence dashboard that monitors social media in real time and helps businesses gauge consumer sentiment.\u003C\/li\u003E\u003Cli\u003EXDATA \u2013 Working with researchers from the School of Computational Science and Engineering, GTRI senior research scientists Barry Drake and Richard Boyd are helping to address big-data challenges by studying the computational demands of processing machine-learning algorithms.\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003E\u003Cbr \/\u003E\u003Cstrong\u003EResearch News\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EGeorgia Institute of Technology\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003E177 North Avenue\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EAtlanta, Georgia\u0026nbsp; 30332-0181\u0026nbsp; USA\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003EMedia Relations Contacts\u003C\/strong\u003E: John Toon (\u003Ca href=\u0022mailto:jtoon@gatech.edu\u0022\u003Ejtoon@gatech.edu\u003C\/a\u003E) (404-894-6986) or Brett Israel (\u003Ca href=\u0022mailto:brett.israel@comm.gatech.edu\u0022\u003Ebrett.israel@comm.gatech.edu\u003C\/a\u003E) (404-385-1933).\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EWriter\u003C\/strong\u003E: Rick Robinson\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cbr \/\u003E\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EResearchers at the Georgia Tech Research Institute (GTRI) are bringing graph analytics to bear on a range of data-related challenges. They\u0027re developing advanced technology that can help investigate social networks, surveillance intelligence, computer-network functionality, industrial control systems, and more.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Researchers at the Georgia Tech Research Institute (GTRI) are bringing graph analytics to bear on a range of data-related challenges."}],"uid":"27303","created_gmt":"2014-11-06 14:47:38","changed_gmt":"2016-10-08 03:17:26","author":"John Toon","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2014-11-06T00:00:00-05:00","iso_date":"2014-11-06T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"342481":{"id":"342481","type":"image","title":"Graph analytics","body":null,"created":"1449245639","gmt_created":"2015-12-04 16:13:59","changed":"1475895062","gmt_changed":"2016-10-08 02:51:02","alt":"Graph analytics","file":{"fid":"200812","name":"graphanalytics1.jpg","image_path":"\/sites\/default\/files\/images\/graphanalytics1_0.jpg","image_full_path":"http:\/\/www.tlwarc.hg.gatech.edu\/\/sites\/default\/files\/images\/graphanalytics1_0.jpg","mime":"image\/jpeg","size":1881657,"path_740":"http:\/\/www.tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/graphanalytics1_0.jpg?itok=SnYpu62m"}}},"media_ids":["342481"],"groups":[{"id":"1188","name":"Research Horizons"}],"categories":[{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"147","name":"Military Technology"},{"id":"135","name":"Research"}],"keywords":[{"id":"15092","name":"big data"},{"id":"438","name":"data"},{"id":"108871","name":"graph analytics"},{"id":"416","name":"GTRI"}],"core_research_areas":[{"id":"39481","name":"National Security"}],"news_room_topics":[{"id":"71881","name":"Science and Technology"}],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJohn Toon\u003C\/p\u003E\u003Cp\u003EResearch News\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:jtoon@gatech.edu\u0022\u003Ejtoon@gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E(404) 894-6986\u003C\/p\u003E","format":"limited_html"}],"email":["jtoon@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}