KDD 2013 - Welcome to Chicago

KDD 2013 - Welcome to Chicago

KDD 2013 will take place August 11-14, 2013 in Chicago, IL, USA

KDD 2013

KDD 2013 will take place August 11-14, 2013 in Chicago, IL, USA

It is a pleasure to welcome you to the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) which will be held in

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2012 SIGKDD Service Award: Dr. Ying Li

Dr. Ying Li is recognized for her substantial technical contributions to the practice and application of data mining and for her outstanding service to the global KDD community. Over the last fifteen years, Dr. Li has actively served the data mining community as a leader, contributor, and mentor to many colleagues and up and coming scientists. 

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2012 SIGKDD Innovation Award: Prof. Vipin Kumar

Prof. Vipin Kumar is recognized for his technical contributions to foundational research in data mining as well as its applications to mining scientific data. Prof. Kumar has made numerous significant and impactful contributions to a wide range of core data mining areas including graph partitioning, clustering, association analysis, high performance and parallel data mining, anomaly/change detection and data driven discovery methods for analyzing global climate and ecosystem data. Many of his papers on these topics are amongst the most highly cited papers in data mining.

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Dr. Ying Li Awarded the 2012 SIGKDD Service Award

Dr. Ying Li Awarded the 2012 SIGKDD Service Award

ACM SIGKDD is pleased to announce that Dr. Ying Li is the winner of its 2012 Service Award.  Dr. Ying Li is recognized for her substantial technical contributions to the practice and application of data mining and for her outstanding service to the global KDD community.

 
ACM SIGKDD Service

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2012 - Big Data: End of the World or End of BI?

2012 - Big Data: End of the World or End of BI?

Thursday, June 28, 2012 at 12:00 PM EDT/11:00 AM CDT/9:00 AM PDT/4:00 PM UTC 

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According to some, the world will end at the Winter Solstice this year. If not, it’s certainly time to take a closer look at Business Intelligence (BI) and ask: “After 20 years,

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KDD 2012: Welcome to Beijing!

KDD 2012: Welcome to Beijing!

We are weeks away from the 18th International ACM SIGKDD Conference, KDD 2012! As we approach early bird registration for the conference, I wanted to take a minute and share with you the importance of the event and encourage you to take advantage of the upcoming early bird registration.

For the

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The Age of Big Data: From KDD-89 to KDD-2012

The Age of Big Data: From KDD-89 to KDD-2012

We live in the age of Big Data - the Second major revolution in modern civilization after the Industrial revolution: the Information Revolution.  Big data is used to improve sales, reduce customer attrition, recommend movies, music, and people, reduce fraud, predict crime, find new drugs, develop

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2011 SIGKDD Service Award: Dr. Bharat Rao

Dr. Rao is being recognized for his pioneering applications of data mining to develop healthcare products that reduce costs and improve the quality of patient care. Dr. Rao and his group have developed and deployed data mining products that analyze millions of patient records, impacting major areas in healthcare. Dr. Rao has a long history of serving the data mining community. He has organized several workshops and conferences, including chairing KDD-2010, mentored several PhD students, and been a member of many editorial boards. 

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2011 SIGKDD Innovation Award: Dr. J. Ross Quinlan

Dr. Ross Quinlan is recognized for his seminal contributions to rule induction and decision tree algorithms and for participating in laying the foundation of data mining, particularly with the invention of ID3 and C4.5, algorithms pivotal in myriad applications. Dr. Ross Quinlan is best known for the development of programs for machine learning and data mining. 

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2010 SIGKDD Service Award: Prof. Osmar R. Zaïane

Prof. Zaïane has a long history of serving the KDD community. In addition to his dedicated service to SIGKDD and its flagship conferences and workshops, he has served the wider KDD community in a wide variety of roles and responsibilities. He has served as technical program co-chair, conference vice-chair, tutorials chair, technical program committee member, exhibits and demos chair, sponsorship chair, proceedings chair, registration chair, publicity chair, webmaster, and other volunteer positions. He currently serves on the steering committee of IEEE ICDM.

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2010 SIGKDD Innovation Award: Dr. Christos Faloutsos

Prof. Christos Faloutsos is recognized for his fundamental contributions to graph and multimedia mining, fractals, self-similarity and power laws; indexing for multimedia and bioinformatics data, and data base performance evaluation. His seminal cross-disciplinary works on power-law graphs, fractal-based analysis, time series, multimedia and spatial indexing are a rare combination of both impressive breadth as well as fundamental depth that set new research directions and inspired subsequent research impacting the KDD field.

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2009 SIGKDD Service Award: Dr. Sunita Sarawagi

Dr. Sunita Sarawagi is well known for her work on information extraction and integration based on statistical learning techniques and multidimensional data analysis. She has contributed open source software for information extraction using Conditional Random Fields, duplication elimination using active learning, and a toolkit called ICube for mining multidimensional OLAP products.

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2009 SIGKDD Innovation Award: Dr. Padhraic Smyth

Padhraic Smyth is recognized for his contributions to both the theory and application of probabilistic and statistical approaches to data mining. Smyth’s research is in the area of statistical data mining and machine learning. His research focuses on both the basic principles of inference from data (theory and algorithms), combined with applications to a variety of data-driven problems in the sciences, medicine, and engineering. 

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2008 SIGKDD Innovation Award: Raghu Ramakrishnan

Ramakrishnan's contributions span foundational technical innovation on algorithmic and systems aspects of data mining. His work on scalable data mining algorithms started with BIRCH, the first truly scalable clustering algorithm. BIRCH introduced the groundbreaking idea of a cluster feature, a concise summary of a cluster, which was then used in many subsequent clustering algorithms as an integral component.

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