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Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)

Chapter: Daryl Pregibon Keynote Address: Graph Mining - Discovery in Large Networks

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Suggested Citation:"Daryl Pregibon Keynote Address: Graph Mining - Discovery in Large Networks ." National Research Council. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11098.
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Page 136

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KEYNOTE ADDRESS: GRAPH MINING—DISCOVERY IN LARGE NETWORKS 136 Daryl Pregibon Keynote Address: Graph Mining—Discovery in Large Networks Abstract of Presentation Transcript of Presentation and PowerPoint Slides BIOSKETCH: Daryl Pregibon is head of the Statistics Research Department at AT&T Shannon Research Labs. His department is responsible for developing a theoretical and computational foundation of statistics for very large data sets. He has been with the Labs for over 20 years. He has interest in and has made contributions to the three main areas of statistics: modeling, data analysis, and computing. His specific contributions include data analytic methods for generalized linear and tree-based models, incorporating statistical expertise in data analysis software, and designing and building application-specific data structures in statistical computing. He is very active in data mining, which he defines as an interdisciplinary field combining statistics, artificial intelligence, and database research. Dr. Pregibon received a PhD in statistics from the University of Toronto in 1979 and an MS in statistics from the University of Waterloo in 1976. He is a fellow of the American Statistical Association and has published over 50 articles in his field. He was coauthor of the best applications paper, “Empirical Bayes Screening for Multi-item Association in Large Databases,” at Knowledge Discovery and Data Mining 2001 (KDD2001) and the best research paper, “Hancock: A Language for Extracting Signatures from Data Streams,” at KDD2000. He is the past chair of CATS (Committee on Applied and Theoretical Statistics, National Academy of Sciences). He was co-chair of KDD97 and has been either a special advisor or member of the KDD program committees for the past 3 years. His is co- founder of SAIAS (Society for Artificial Intelligence and Statistics). Currently he is a member of CNSTAT (Committee on National Statistics, National Academy of Sciences), a member of the SIGKDD Executive Committee, a member of the Steering Committee of IDA (Intelligent Data Analysis), and a member of the Editorial Board of Data Mining and Knowledge Discovery.

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Massive data streams, large quantities of data that arrive continuously, are becoming increasingly commonplace in many areas of science and technology. Consequently development of analytical methods for such streams is of growing importance. To address this issue, the National Security Agency asked the NRC to hold a workshop to explore methods for analysis of streams of data so as to stimulate progress in the field. This report presents the results of that workshop. It provides presentations that focused on five different research areas where massive data streams are present: atmospheric and meteorological data; high-energy physics; integrated data systems; network traffic; and mining commercial data streams. The goals of the report are to improve communication among researchers in the field and to increase relevant statistical science activity.

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